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Research Article | DOI: https://doi.org/10.31579/2578-8868/378
Head of Neurosurgical Department, Juaneda Miramar, Palma de Mallorca, Balearic Islands, Spain.
*Corresponding Author: Gurneet Kaur, Head of Neurosurgical Department, Juaneda Miramar, Palma de Mallorca, Balearic Islands, Spain.
Citation: Gurneet Kaur, Sukhwinder Singh (2025), Neonatal Seizure Detection from EEG Signals using Deep Learning Methods, J. Neuroscience and Neurological Surgery, 17(5); DOI:10.31579/2578-8868/378
Copyright: © 2025, Gonçalo Januário. This is an open-access article distributed under the terms of The Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Received: 09 May 2025 | Accepted: 30 May 2025 | Published: 26 June 2025
Keywords: epilepsy ; electroencephalography ; deep learning ; wavelet decomposition ; daubechies transform ; convolutional neural networks
Neonatal epilepsy, a serious neurological condition in newborns, is typically marked by irregular brain activity that results in repeated seizures. This could have detrimental effect on neurodevelopment and may raise risk of cognitive and behavioral abnormalities. In order to prevent long-term neurological damage and developmental delays, an accurate detection of such seizures is crucial. Neonatal seizures can be subtle and may not exhibit any clear physical signs and hence, complicating timely diagnosis and treatment. To assist in diagnosis, electroencephalogram (EEG) can analyze, but manual inspection is time-consuming and burdensome for neurologists. Therefore, an automated EEG review would enable more frequent monitoring of neonates at risk. Hence, an automated system for detecting seizures has been created, trained on multichannel annotated EEG recordings from 79 full-term neonates who were admitted to Helsinki University Hospital. The system extracts wavelet coefficients from 10 seconds EEG segments using multilevel Daubechies 4 (db4) wavelet decompositionand uses these features to train a 2-Dimensional Convolutional Neural Network (2D-CNN). This model has achieved 100% accuracy, sensitivity, and F1-score, outperformed existing state-of-the-art methods and provided a reliable solution for automated seizure detection.
Neonatal seizures, the most commonly encountered neurological disorder often indicating underlying brain dysfunction. The neonatal period is usually considered to be the first 4 weeks/28 days of a full-term newborn’s life. This is critical stage of brain growth and development [1]. When compared with adults and children, it was found that neonates are more susceptible to seizures because of incomplete inhibitory control mechanisms and a higher ratio of excitatory neurotransmitters. These seizures carry significant risks, including neuronal damage and long-term neurodevelopmental impairments. It is estimated that around 7-10% of neonates are at a risk of death while, 23-50% are likely to develop some abnormality [2]. Unlike seizures in older populations, neonatal seizures often lack physical signs, making diagnosis particularly difficult [3]. While various neuro-imaging and signal processing techniques have been explored, electroencephalography (EEG) remains the benchmark for seizure detection particularly in neonates due to its cost-effective, noninvasive nature and remarkable capability to capture real-time electrophysiological activity. However, interpreting neonatal EEG requires considerable clinical expertise and is also susceptible to variability and subjectivity in experts’ opinions [4,5]. This issue led to a burgeoning development of automated, computer-aided seizure detection systems.
Early approaches focused on long-established data-driven machine learning (ML) techniques.
Machine learning-based classifiers were mostly fed features are extracted through various domain analysis such as time or frequency [6]. Machine learning approaches mainly comprises two parts: feature extraction and classification. Classifiers like Naïve Bayes classifiers, Support Vector Machines (SVMs) and Random Forests are commonly used [7]. Numerous techniques like Discrete Wavelet Transform, Principal Component Analysis, and signal chunking have been used for feature extraction. Many studies used above techniques and achieved accuracies up to 99% with Naive Bayes classifier [8], and around 100% using SVM classifiers [9]. For instance, Biswal et al. [10] utilized a Naïve Bayes classifier to analyze 3,277 EEG reports, which were categorized based on the presence or absence of epileptiform discharges or seizures. This approach resulted in an AUC of 99.05%. Meanwhile, Runarsson et al. [11] created a real-time neonatal seizures detection system using Support Vector Machines (SVM) and half-wave attribute histograms. This system demonstrated high specificity (up to 100%) and sensitivity (over 80%). However, the requirement of handcrafted features can limit scalability and generalizability in machine learning methods.
Recent advances in arena of deep learning (DL) had considerably improved performance in automated seizure detection. Models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid architectures such as CNN-LSTM and CNN-RNN, can automatically extract and learn useful features from raw EEG data [12]. Gramacki et al. [13] trained a deep learning framework on chunks of EEG signals obtained from sliding window technique, resulting in an average accuracy of 96-97% on a Helsinki dataset. Another study by Hogan et al. [ 2] used same dataset and trained a CNN model and achieved AUC as 0.982. Whereas, an approach by O'Shea et al. [14] used two distinct CNNs on a large clinical dataset, and found out that the 11-layers deep architecture significantly surpassed the performance of the shallower architectures, boosting the accuracy from 82.6% to 86.8%. Furthermore, O'Shea et al. [15] proposed a fully CNN model on raw multi-channel EEG signals leveraging weakly labeled data to enhance training efficiency and achieved an high accuracy of 98.5%. To determine the severity levels of neonatal epileptic episodes in actual medical dataset, Debelo et al. [16] employed a deep CNN which was quite efficient with accuracy, specificity and precision to be around 92%. An innovative 1D-CNN model was suggested by Sameer et al. [17] with remarkable accuracy up to 99.83% using only an average of seven epochs for training resulting in a substantial reduction in training time. Li et al. [18] introduced a 1-dimensional convolutional neural network (1D-CNN) model that incorporates meta-learning, which involves continuously adjusting the model's weights until an optimal weight configuration is reached. This model achieved an average performance of over 92.63% in sensitivity, specificity, and F1 score. Meanwhile, Ullah et al. [19] proposed a pyramidal 1D-CNN to address the challenge of a large number of learnable parameters, as it uses 61
This research utilizes a neonatal EEG dataset comprising multi-channel EEG recordings from 79 neonates. These infants were admitted to the NICU at Helsinki University Hospital, Finland, between 2010 and 2014[26]. The EEG recordings were conducted using 19 electrodes [30] according to the "International 10–20 system (Fp1, Fp2, F3, F4, F7, F8, Fz, C3, C4, Cz, P3, P4, Pz, T3, T4, T5, T6, O1, O2)", with 256Hz as
sampling rate and a one-second resolution, measured in microvolts (µV)[30]. Three experts independently annotated the EEG recordings, and during this procedure, the signals were in a standard bipolar montage ('double banana'). Figure 1A shows the electrode arrangement in standard bipolar montage. Among the neonates, 40 had seizures confirmed by all experts, 17 had experienced seizures as identified by one or two experts, and 22 were free of seizures. In total, 1,379 seizures were annotated. Annotation details are provided in Table 1A. The dataset is publicly accessible at [31].
Figure 1A: Full 18-channel (double banana) montage.[41]
| EXP3 | EXP12 | EXP0 | |||||||||
| Infant # | A | B | C | Infant # | A | B | C | Infant # | A | B | C |
| 1 | 25 | 42 | 36 | 2 | 2 | - | - | 3 | - | - | - |
| 4 | 2 | 7 | 3 | 6 | - | - | 4 | 10 | - | - | - |
| 5 | 5 | 5 | 7 | 8 | 1 | 2 | - | 18 | - | - | - |
| 7 | 6 | 21 | 6 | 12 | - | - | 1 | 27 | - | - | - |
| 9 | 3 | 8 | 3 | 23 | 4 | - | 9 | 28 | - | - | - |
| 11 | 3 | 4 | 1 | 24 | - | 1 | - | 29 | - | - | - |
| 13 | 5 | 6 | 6 | 26 | - | - | 7 | 30 | - | - | - |
| 14 | 45 | 26 | 38 | 33 | 1 | - | 6 | 32 | - | - | - |
| 15 | 19 | 6 | 20 | 43 | - | - | 4 | 35 | - | - | - |
| 16 | 30 | 45 | 61 | 46 | - | - | 4 | 37 | - | - | - |
| 17 | 4 | 3 | 3 | 54 | 8 | - | 17 | 42 | - | - | - |
| 19 | 9 | 13 | 10 | 56 | - | - | 1 | 45 | - | - | - |
| 20 | 17 | 19 | 24 | 61 | - | - | 3 | 48 | - | - | - |
| 21 | 1 | 1 | 1 | 64 | - | 25 | 12 | 49 | - | - | - |
| 22 | 8 | 6 | 7 | 65 | - | - | 4 | 53 | - | - | - |
| 25 | 12 | 14 | 4 | 68 | 1 | 2 | - | 55 | - | - | - |
| 31 | 2 | 2 | 2 | 74 | - | 5 | 6 | 57 | - | - | - |
| 34 | 1 | 1 | 1 | Sum EXP12 | 58 | - | - | - | |||
| 36 | 2 | 1 | 2 | 17 | 35 | 78 | 59 | - | - | - | |
| 38 | 19 | 16 | 24 | 60 | - | - | - | ||||
| 39 | 6 | 7 | 6 | 70 | - | - | - | ||||
| 40 | 12 | 18 | 8 | 71 | - | - | - | ||||
| 41 | 45 | 10 | 58 | EXP0 | |||||||
| 44 | 7 | 10 | 8 | 0 | 0 | 0 | |||||
| 47 | 3 | 5 | 3 | ||||||||
| 50 | 10 | 10 | 10 | ||||||||
| 51 | 4 | 1 | 8 | ||||||||
| 52 | 1 | 2 | 2 | ||||||||
| 62 | 1 | 1 | 1 | ||||||||
| 63 | 5 | 12 | 25 | ||||||||
| 66 | 2 | 2 | 2 | ||||||||
| 67 | 16 | 16 | 19 | ||||||||
| 69 | 14 | 9 | 15 | ||||||||
| 71 | 4 | 4 | 2 | ||||||||
| 73 | 6 | 4 | 7 | ||||||||
| 75 | 1 | 1 | 1 | ||||||||
| 76 | 2 | 3 | 3 | ||||||||
| 77 | 1 | 3 | 3 | ||||||||
| 78 | 22 | 22 | 24 | ||||||||
| 79 | 5 | 8 | 6 | ||||||||
| EXP3 | |||||||||||
| 385 | 394 | 470 | |||||||||
Table 1A: Numbers of seizures annotated by 3 experts (marked as A, B and C) for every infant [13],
The dataset includes 79 edf files with information of EEG recordings and 3 annotation files corresponding to each expert [13]. The raw signals were in unipolar montage and hence, could not be used directly; instead, a standard bipolar montage was created using 18 electrode pairs such as “Fp2-F4, F4-C4, C4-P4, P4-O2, Fp1-F3, F3-C3, C3-P3, P3-O1, Fp2-F8, F8-T4, T4-T6, T6-O2, Fp1-F7, F7-T3, T3-T5,
T5-O1, Fz-Cz, Cz-Pz”. In this research, data from 40 neonates, each annotated by all three experts, and 17 neonates, annotated by one or two experts, were chosen. To ensure consistency, only seizure events agreed upon by at least two experts were retained. For example, if two out of three experts labeled a time segment as a seizure, it was accepted as such in the final dataset. To segment the EEG signals, a sliding window approach was used, a common method in seizure detection studies [13,32,33]. A window size of 10 seconds with 1-second overlap was applied for seizure segments. Non-seizure segments used non- overlapping windows. This resulted in 5,294 seizure and 19,858 non-seizure windows, saved in CSV format. For feature extraction, each EEG window was processed using the Discrete Wavelet Transform (DWT) and further was decomposed using Daubechies 4 (db4) wavelet [34]. The signal was decomposed into four levels, separating it into approximation (A) coefficients and detail (D) coefficients. The model uses coefficients A4, D4, D3, and D2, which correspond to the delta and theta bands(A4), alpha band(D4), beta band(D3) and gamma band(D2) respectively. This decomposition helps isolate key frequency components relevant to seizure detection, as illustrated in Figure 1B. Each 10-second EEG window, sampled at 256 Hz, contains 2,560 samples. Using Daubechies-4 (db4) wavelet decomposition upto 4 levels [34], the signal is split into approximation (A4) and detail coefficients (D2, D3, D4), resulting in 2,560 coefficients per window (A4: 160, D4: 160, D3: 320, D2: 640, plus D1: 1,280). With 18 channels, each window forms an 18 × 2,560 feature matrix. The dataset was divided into a 70:30 ratio, with 70% reserved for the training set and 30% designated for the testing set. The features were reshaped into 3D tensors for CNN input—specifically, the dimension of a single tensor was 20 × 2,560 × 18 with corresponding label vectors of size 20 × 1. Each tensor contains 10 seizure and 10 non-seizure windows. This transformation from 1D to 3D is essential for compatibility with 2D-CNN model. The tensors formed are visually represented in Figures 1C and 1D. Figure 1C represents a single layer of a tensor with dimension 20 X 2560 where 20(10+10) is number of seizure and non-seizure instances and 2560 are number of wavelet coefficients. Figure 1B represents a single tensor with 18 layers depth-wise representing 18 channels.
The architecture of the model, drawing inspiration from Gramacki et al. [13], is composed of three blocks
Figure 1B: Wavelet Decomposition using db4
Figure. 1C: A depiction of Tensor layer and label vector
Figure 1D: The 3D tensor fed to the CNN model.
Each block includes a Conv2D layer, a batch normalization layer, a ReLU activation function, a max pooling layer, and a dropout layer [13]. The architecture is represented in Figure 1E. The filter count starts at 128 in the first block, decreases to 64 in the second, and further reduces to 32 in the third block. The dropout rate is set at 25% for the first two blocks, while the final convolutional block has a dropout rate of 50%. Following
the convolutional layers, there are two dense layers, one with 64 units and the other with 32 units, both employing L2 regularization. This is succeeded by a dense layer that gives a single unit output. The model utilizes binary cross-entropy as its loss function. It operates at 0.001 learning rate using the Adam optimizer and was trained for 300 epochs with batch size of 32. An elaborated summary of the model parameters is provided in Table 1B.
Figure 1E: Architecture of proposedmodel
| Parameter | Value |
| Optimization Algorithm | Adam Optimizer |
| Activation Function | Sigmoid |
| Loss Function | Binary Cross Entropy |
| Learning Rate | 0.001 |
| Batch Size | 32 |
| Epochs | 300 |
Table 1B: Model Parameters
The most common metrics for assessing model performance include accuracy, sensitivity, and specificity [13]. These metrics are based on a confusion matrix, which demonstrates the number of correct and incorrect predictions for each class. Predictions are typically categorized as “True Positive (TP)” (accurately predicts the positive class), “True Negative (TN)” (accurately predicts the negative class), “False Positive (FP)” (wrongly predicts the negative class), and “False Negative (FN)” (wrongly predicts the positive class). The formulas for calculating accuracy, sensitivity, and specificity are represented as follows [35]:
The proposed method was evaluated using the same performance metrics described earlier. The extracted features were fed into the model, and after training, the output layer produced a vector of size 20×1. In this vector, each component had a value between 0 and 1. To improve the model's assessment, a threshold of 0.5 was used: elements with values less than or equal to 0.5 were assigned a classification of 0, while those with values above 0.5 were classified as 1. It is important to highlight that the output elements were mostly found within the intervals [0, 0.3] or [0.7, 1]. The threshold facilitates a clearer distinction between actual and predicted outputs. This step improved the comparability of the results. The accuracy and loss values obtained by the proposed model in training, testing and validation phases are summarized in Table 2A. The training accuracy along with testing accuracy was obtained as 100% whereas, the loss was approximately approaching to 0%. The slight ambiguity in the loss values can be attributed to the penalty term introduced by the L2 regularizer. Figures 2A and 2B depict the confusion matrices for the training phase and testing phase, respectively. By calculating the evaluation metrics from confusion metrics, the model achieved 100 % accuracy and sensitivity, with an F1 score of 1. Additionally, Figures 2C and 2D illustrate the accuracy curve and loss curve during the training phase. The graphs indicate fluctuations in the early epochs; however, the curves quickly stabilized, reaching nearly 100%.
Figure. 2A: Confusion Matrix for training Phase.
Figure 2B: Confusion Matrix for testingPhase.
Figure2C: AccuracyCurve for trainingphase
Figure.2D: Loss Curve for training phase
| Accuracy | Loss | |
| Training | 1.0 | 0.0005 |
| Validation | 1.0 | 0.0005 |
| Testing | 1.0 | 0.0004 |
Table 2A: Accuracy and Loss values in various phases.
The performance of proposed the CNN-based model is capable of accurately distinguishing between seizure and non-seizure occurrences in EEG signals by utilizing features derived from wavelet transformations. The model achieved perfect classification performance, with 100% accuracy, sensitivity, and an F1 score of 1 in training and as well as testing phase. This indicates a strong generalization capability, likely aided by the distinct separation of output probabilities and the robustness of the chosen wavelet features. The use of dropout layers and L2 regularization effectively mitigated over-fitting, as evident from the stable loss and accuracy curves.
Numerous other approaches have been devised to detect eplileptic seizures. To evaluate the effectiveness of the proposed method, a comparison was conducted with several existing deep learning techniques using the same dataset. The baseline model by Gramacki et al. [13] employed a 10-second sliding window technique to extract features and form sub-datasets, which were then fed to CNN, yielding an average accuracy of 96–97% across various sub-datasets.Tanveer et al. [26] utilized a combination of three 2-D models, achieving an accuracy rate of 96.3%. Visalini et al. [28] implemented a “triplet half-band filter” along with “wavelet packet decomposition” before inputting the data into a Deep Belief Network, which resulted in an accuracy of 98.7%. Raeisi et al. [29] introduced a “Spatio-Temporal Graph Attention Network (ST-GAT)” that effectively captures both temporal and spatial characteristics, achieving 96.6% accuracy on the Helsinki dataset [31]. Daly et al. [36] introduced a deep neural network leveraging longer EEG segments, data augmentation, residual connections, and a robust optimizer, achieving 97.73% accuracy on over 4,570 hours of clinical EEG recordings. The results are summarized in Table 3A. While these methods demonstrate high accuracy, none achieved perfect classification. In contrast, the proposed method reached 100% accuracy, sensitivity, and F1 score, highlighting its superior performance on the same dataset.
Several other studies have also explored time-frequency domain analysis for EEG signal classification; however, their reported accuracies were often not satisfactory. As shown in Table 3B, Cho et al. [37].
| Authors | Methods | Accuracy |
| Gramacki et al. [4] | Sliding window design and CNN model | 96-97% |
| Tanveer et al. [21] | Ensemble model using 3 different CNN model | 96.3% |
| Visalini et al. [23] | Deep BeliefNetwork with Triplet Half-Band filter and Wavelet Packet Decomposition | 98.7 % |
| Raeisi et al. [24] | Spatio-Temporal Graph Attention Network (ST-GAT) | 96.6 % |
| Daly et al. [30] | CNN withresidual connections anddata augmentation | 97.73% |
Table 3A: Comparison with existing methods using same dataset
| Authors | Methods | Accuracy |
| Cho et al. [31] | Wavelet Transform and SVM | 80.54% |
| Khan et al. [32] | Discrete Wavelet Tranform and CNN model | 87.8% |
| Sharma et al. [33] | Analytic time-frequency flexible wavelet transforms and fractal dimension and LS-SVM | 100% |
| Thasneem et al. [34] | Wavelet based features and linear classifier | 99.5% |
Table 3B: Comparison with existing methods using time-frequency analysis.
Applied wavelet transform followed by Support Vector Machine (SVM) classification, resulting in 80.54% accuracy. Thasneem et al. [38] improved on this by combining wavelet-based features with a linear classifier, reaching 99.5% accuracy. Some studies employed approaches particularly wavelet analysis comparable to the proposed method. For instance, Khan et al. [39] extracted features using discrete wavelet transform and classified the data using a CNN, but achieved a lower accuracy of 87.8%. While Sharma et al. [40] reported 100% accuracy, their method differed considerably, incorporating machine learning models with a flexible wavelet transform and fractal dimension-based features.
The suggested method streamlines the classification process by employing a simplified deep learning framework, achieving results that are either on par with or better than those obtained through intricate preprocessing or manually crafted feature extraction.
Several methods have been developed for detecting neonatal seizures using EEG data. Before the advent of deep learning, traditional machine learning algorithms were widely employed, utilizing features derived from various domains for analysis. In this study, wavelet decomposition using the Daubechies 4 transform was performed on a 10 second window of EEG data. This data was split into a 70:30 ratio where, 70% is used for training and 30% is reserved for testing. The proposed method achieved 100
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Dear editorial department: On behalf of our team, I hereby certify the reliability and superiority of the International Journal of Clinical Case Reports and Reviews in the peer review process, editorial support, and journal quality. Firstly, the peer review process of the International Journal of Clinical Case Reports and Reviews is rigorous, fair, transparent, fast, and of high quality. The editorial department invites experts from relevant fields as anonymous reviewers to review all submitted manuscripts. These experts have rich academic backgrounds and experience, and can accurately evaluate the academic quality, originality, and suitability of manuscripts. The editorial department is committed to ensuring the rigor of the peer review process, while also making every effort to ensure a fast review cycle to meet the needs of authors and the academic community. Secondly, the editorial team of the International Journal of Clinical Case Reports and Reviews is composed of a group of senior scholars and professionals with rich experience and professional knowledge in related fields. The editorial department is committed to assisting authors in improving their manuscripts, ensuring their academic accuracy, clarity, and completeness. Editors actively collaborate with authors, providing useful suggestions and feedback to promote the improvement and development of the manuscript. We believe that the support of the editorial department is one of the key factors in ensuring the quality of the journal. Finally, the International Journal of Clinical Case Reports and Reviews is renowned for its high- quality articles and strict academic standards. The editorial department is committed to publishing innovative and academically valuable research results to promote the development and progress of related fields. The International Journal of Clinical Case Reports and Reviews is reasonably priced and ensures excellent service and quality ratio, allowing authors to obtain high-level academic publishing opportunities in an affordable manner. I hereby solemnly declare that the International Journal of Clinical Case Reports and Reviews has a high level of credibility and superiority in terms of peer review process, editorial support, reasonable fees, and journal quality. Sincerely, Rui Tao.
Clinical Cardiology and Cardiovascular Interventions I testity the covering of the peer review process, support from the editorial office, and quality of the journal.
Clinical Cardiology and Cardiovascular Interventions, we deeply appreciate the interest shown in our work and its publication. It has been a true pleasure to collaborate with you. The peer review process, as well as the support provided by the editorial office, have been exceptional, and the quality of the journal is very high, which was a determining factor in our decision to publish with you.
The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews journal clinically in the future time.
Clinical Cardiology and Cardiovascular Interventions, I would like to express my sincerest gratitude for the trust placed in our team for the publication in your journal. It has been a true pleasure to collaborate with you on this project. I am pleased to inform you that both the peer review process and the attention from the editorial coordination have been excellent. Your team has worked with dedication and professionalism to ensure that your publication meets the highest standards of quality. We are confident that this collaboration will result in mutual success, and we are eager to see the fruits of this shared effort.
Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, I hope this message finds you well. I want to express my utmost gratitude for your excellent work and for the dedication and speed in the publication process of my article titled "Navigating Innovation: Qualitative Insights on Using Technology for Health Education in Acute Coronary Syndrome Patients." I am very satisfied with the peer review process, the support from the editorial office, and the quality of the journal. I hope we can maintain our scientific relationship in the long term.
Dear Monica Gissare, - Editorial Coordinator of Nutrition and Food Processing. ¨My testimony with you is truly professional, with a positive response regarding the follow-up of the article and its review, you took into account my qualities and the importance of the topic¨.
Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, The review process for the article “The Handling of Anti-aggregants and Anticoagulants in the Oncologic Heart Patient Submitted to Surgery” was extremely rigorous and detailed. From the initial submission to the final acceptance, the editorial team at the “Journal of Clinical Cardiology and Cardiovascular Interventions” demonstrated a high level of professionalism and dedication. The reviewers provided constructive and detailed feedback, which was essential for improving the quality of our work. Communication was always clear and efficient, ensuring that all our questions were promptly addressed. The quality of the “Journal of Clinical Cardiology and Cardiovascular Interventions” is undeniable. It is a peer-reviewed, open-access publication dedicated exclusively to disseminating high-quality research in the field of clinical cardiology and cardiovascular interventions. The journal's impact factor is currently under evaluation, and it is indexed in reputable databases, which further reinforces its credibility and relevance in the scientific field. I highly recommend this journal to researchers looking for a reputable platform to publish their studies.
Dear Editorial Coordinator of the Journal of Nutrition and Food Processing! "I would like to thank the Journal of Nutrition and Food Processing for including and publishing my article. The peer review process was very quick, movement and precise. The Editorial Board has done an extremely conscientious job with much help, valuable comments and advices. I find the journal very valuable from a professional point of view, thank you very much for allowing me to be part of it and I would like to participate in the future!”
Dealing with The Journal of Neurology and Neurological Surgery was very smooth and comprehensive. The office staff took time to address my needs and the response from editors and the office was prompt and fair. I certainly hope to publish with this journal again.Their professionalism is apparent and more than satisfactory. Susan Weiner
My Testimonial Covering as fellowing: Lin-Show Chin. The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews.
My experience publishing in Psychology and Mental Health Care was exceptional. The peer review process was rigorous and constructive, with reviewers providing valuable insights that helped enhance the quality of our work. The editorial team was highly supportive and responsive, making the submission process smooth and efficient. The journal's commitment to high standards and academic rigor makes it a respected platform for quality research. I am grateful for the opportunity to publish in such a reputable journal.
My experience publishing in International Journal of Clinical Case Reports and Reviews was exceptional. I Come forth to Provide a Testimonial Covering the Peer Review Process and the editorial office for the Professional and Impartial Evaluation of the Manuscript.
I would like to offer my testimony in the support. I have received through the peer review process and support the editorial office where they are to support young authors like me, encourage them to publish their work in your esteemed journals, and globalize and share knowledge globally. I really appreciate your journal, peer review, and editorial office.
Dear Agrippa Hilda- Editorial Coordinator of Journal of Neuroscience and Neurological Surgery, "The peer review process was very quick and of high quality, which can also be seen in the articles in the journal. The collaboration with the editorial office was very good."
I would like to express my sincere gratitude for the support and efficiency provided by the editorial office throughout the publication process of my article, “Delayed Vulvar Metastases from Rectal Carcinoma: A Case Report.” I greatly appreciate the assistance and guidance I received from your team, which made the entire process smooth and efficient. The peer review process was thorough and constructive, contributing to the overall quality of the final article. I am very grateful for the high level of professionalism and commitment shown by the editorial staff, and I look forward to maintaining a long-term collaboration with the International Journal of Clinical Case Reports and Reviews.
To Dear Erin Aust, I would like to express my heartfelt appreciation for the opportunity to have my work published in this esteemed journal. The entire publication process was smooth and well-organized, and I am extremely satisfied with the final result. The Editorial Team demonstrated the utmost professionalism, providing prompt and insightful feedback throughout the review process. Their clear communication and constructive suggestions were invaluable in enhancing my manuscript, and their meticulous attention to detail and dedication to quality are truly commendable. Additionally, the support from the Editorial Office was exceptional. From the initial submission to the final publication, I was guided through every step of the process with great care and professionalism. The team's responsiveness and assistance made the entire experience both easy and stress-free. I am also deeply impressed by the quality and reputation of the journal. It is an honor to have my research featured in such a respected publication, and I am confident that it will make a meaningful contribution to the field.
"I am grateful for the opportunity of contributing to [International Journal of Clinical Case Reports and Reviews] and for the rigorous review process that enhances the quality of research published in your esteemed journal. I sincerely appreciate the time and effort of your team who have dedicatedly helped me in improvising changes and modifying my manuscript. The insightful comments and constructive feedback provided have been invaluable in refining and strengthening my work".
I thank the ‘Journal of Clinical Research and Reports’ for accepting this article for publication. This is a rigorously peer reviewed journal which is on all major global scientific data bases. I note the review process was prompt, thorough and professionally critical. It gave us an insight into a number of important scientific/statistical issues. The review prompted us to review the relevant literature again and look at the limitations of the study. The peer reviewers were open, clear in the instructions and the editorial team was very prompt in their communication. This journal certainly publishes quality research articles. I would recommend the journal for any future publications.
Dear Jessica Magne, with gratitude for the joint work. Fast process of receiving and processing the submitted scientific materials in “Clinical Cardiology and Cardiovascular Interventions”. High level of competence of the editors with clear and correct recommendations and ideas for enriching the article.
We found the peer review process quick and positive in its input. The support from the editorial officer has been very agile, always with the intention of improving the article and taking into account our subsequent corrections.
My article, titled 'No Way Out of the Smartphone Epidemic Without Considering the Insights of Brain Research,' has been republished in the International Journal of Clinical Case Reports and Reviews. The review process was seamless and professional, with the editors being both friendly and supportive. I am deeply grateful for their efforts.
To Dear Erin Aust – Editorial Coordinator of Journal of General Medicine and Clinical Practice! I declare that I am absolutely satisfied with your work carried out with great competence in following the manuscript during the various stages from its receipt, during the revision process to the final acceptance for publication. Thank Prof. Elvira Farina
Dear Jessica, and the super professional team of the ‘Clinical Cardiology and Cardiovascular Interventions’ I am sincerely grateful to the coordinated work of the journal team for the no problem with the submission of my manuscript: “Cardiometabolic Disorders in A Pregnant Woman with Severe Preeclampsia on the Background of Morbid Obesity (Case Report).” The review process by 5 experts was fast, and the comments were professional, which made it more specific and academic, and the process of publication and presentation of the article was excellent. I recommend that my colleagues publish articles in this journal, and I am interested in further scientific cooperation. Sincerely and best wishes, Dr. Oleg Golyanovskiy.
Dear Ashley Rosa, Editorial Coordinator of the journal - Psychology and Mental Health Care. " The process of obtaining publication of my article in the Psychology and Mental Health Journal was positive in all areas. The peer review process resulted in a number of valuable comments, the editorial process was collaborative and timely, and the quality of this journal has been quickly noticed, resulting in alternative journals contacting me to publish with them." Warm regards, Susan Anne Smith, PhD. Australian Breastfeeding Association.
Dear Jessica Magne, Editorial Coordinator, Clinical Cardiology and Cardiovascular Interventions, Auctores Publishing LLC. I appreciate the journal (JCCI) editorial office support, the entire team leads were always ready to help, not only on technical front but also on thorough process. Also, I should thank dear reviewers’ attention to detail and creative approach to teach me and bring new insights by their comments. Surely, more discussions and introduction of other hemodynamic devices would provide better prevention and management of shock states. Your efforts and dedication in presenting educational materials in this journal are commendable. Best wishes from, Farahnaz Fallahian.
Dear Maria Emerson, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews, Auctores Publishing LLC. I am delighted to have published our manuscript, "Acute Colonic Pseudo-Obstruction (ACPO): A rare but serious complication following caesarean section." I want to thank the editorial team, especially Maria Emerson, for their prompt review of the manuscript, quick responses to queries, and overall support. Yours sincerely Dr. Victor Olagundoye.
Dear Ashley Rosa, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews. Many thanks for publishing this manuscript after I lost confidence the editors were most helpful, more than other journals Best wishes from, Susan Anne Smith, PhD. Australian Breastfeeding Association.
Dear Agrippa Hilda, Editorial Coordinator, Journal of Neuroscience and Neurological Surgery. The entire process including article submission, review, revision, and publication was extremely easy. The journal editor was prompt and helpful, and the reviewers contributed to the quality of the paper. Thank you so much! Eric Nussbaum, MD
Dr Hala Al Shaikh This is to acknowledge that the peer review process for the article ’ A Novel Gnrh1 Gene Mutation in Four Omani Male Siblings, Presentation and Management ’ sent to the International Journal of Clinical Case Reports and Reviews was quick and smooth. The editorial office was prompt with easy communication.
Dear Erin Aust, Editorial Coordinator, Journal of General Medicine and Clinical Practice. We are pleased to share our experience with the “Journal of General Medicine and Clinical Practice”, following the successful publication of our article. The peer review process was thorough and constructive, helping to improve the clarity and quality of the manuscript. We are especially thankful to Ms. Erin Aust, the Editorial Coordinator, for her prompt communication and continuous support throughout the process. Her professionalism ensured a smooth and efficient publication experience. The journal upholds high editorial standards, and we highly recommend it to fellow researchers seeking a credible platform for their work. Best wishes By, Dr. Rakhi Mishra.
Dear Jessica Magne, Editorial Coordinator, Clinical Cardiology and Cardiovascular Interventions, Auctores Publishing LLC. The peer review process of the journal of Clinical Cardiology and Cardiovascular Interventions was excellent and fast, as was the support of the editorial office and the quality of the journal. Kind regards Walter F. Riesen Prof. Dr. Dr. h.c. Walter F. Riesen.
Dear Ashley Rosa, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews, Auctores Publishing LLC. Thank you for publishing our article, Exploring Clozapine's Efficacy in Managing Aggression: A Multiple Single-Case Study in Forensic Psychiatry in the international journal of clinical case reports and reviews. We found the peer review process very professional and efficient. The comments were constructive, and the whole process was efficient. On behalf of the co-authors, I would like to thank you for publishing this article. With regards, Dr. Jelle R. Lettinga.
Dear Clarissa Eric, Editorial Coordinator, Journal of Clinical Case Reports and Studies, I would like to express my deep admiration for the exceptional professionalism demonstrated by your journal. I am thoroughly impressed by the speed of the editorial process, the substantive and insightful reviews, and the meticulous preparation of the manuscript for publication. Additionally, I greatly appreciate the courteous and immediate responses from your editorial office to all my inquiries. Best Regards, Dariusz Ziora
Dear Chrystine Mejia, Editorial Coordinator, Journal of Neurodegeneration and Neurorehabilitation, Auctores Publishing LLC, We would like to thank the editorial team for the smooth and high-quality communication leading up to the publication of our article in the Journal of Neurodegeneration and Neurorehabilitation. The reviewers have extensive knowledge in the field, and their relevant questions helped to add value to our publication. Kind regards, Dr. Ravi Shrivastava.
Dear Clarissa Eric, Editorial Coordinator, Journal of Clinical Case Reports and Studies, Auctores Publishing LLC, USA Office: +1-(302)-520-2644. I would like to express my sincere appreciation for the efficient and professional handling of my case report by the ‘Journal of Clinical Case Reports and Studies’. The peer review process was not only fast but also highly constructive—the reviewers’ comments were clear, relevant, and greatly helped me improve the quality and clarity of my manuscript. I also received excellent support from the editorial office throughout the process. Communication was smooth and timely, and I felt well guided at every stage, from submission to publication. The overall quality and rigor of the journal are truly commendable. I am pleased to have published my work with Journal of Clinical Case Reports and Studies, and I look forward to future opportunities for collaboration. Sincerely, Aline Tollet, UCLouvain.
