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Diagnostics
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Deep Learning in Biomedical Signal Analysis
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Deep Learning in Biomedical Signal Analysis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1209

Special Issue Editor

Dr. Takahiro Emoto

E-Mail Website
Guest Editor
Graduate School of Sciences and Technology, Tokushima University, Tokushima, Japan
Interests: biomedical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the application and advancements of deep learning techniques in the field of biomedical signal processing. With the explosive growth of biomedical data and the complexity of biological systems, traditional signal processing methods are often insufficient to extract meaningful insights from these data. Deep learning, as a powerful tool for automatic feature learning and pattern recognition, has demonstrated remarkable performance in analyzing biomedical signals such as electrocardiograms, electroencephalograms, and biosensor data. This Special Issue aims to showcase the latest research on how deep learning can be applied to address challenges in biomedical signal analysis, disease diagnosis, and patient monitoring. Contributions from various domains, including signal processing, machine learning, computer vision, and healthcare, will be welcome to explore the full potential of deep learning in transforming biomedical signal research.

Dr. Takahiro Emoto
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • deep learning
  • biomedical signal processing
  • pattern recognition
  • disease diagnosis
  • feature extraction

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Published Papers (2 papers)

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Research

20 pages, 4006 KiB  
Article
EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis
by Zhi-Yang Zhao, Chang-Ling Huang, Tong-Min Wang, Shi-Hao Zhou, Lu Pei, Wen-Hui Jia and Wei-Hua Jia
Diagnostics 2025, 15(9), 1156; https://doi.org/10.3390/diagnostics15091156 - 1 May 2025
Viewed by 358
Abstract
Background and Objectives: The accurate discrimination between patients with and without cancer using their cell-free DNA (cfDNA) is crucial for early cancer diagnosis. The end-motifs of cfDNA serve as significant cancer biomarkers, offering compelling prospects for cancer diagnosis. This study proposes EM-DeepSD, a [...] Read more.
Background and Objectives: The accurate discrimination between patients with and without cancer using their cell-free DNA (cfDNA) is crucial for early cancer diagnosis. The end-motifs of cfDNA serve as significant cancer biomarkers, offering compelling prospects for cancer diagnosis. This study proposes EM-DeepSD, a signal decomposition deep learning framework based on cfDNA end-motifs, which is aimed at improving the accuracy of cancer diagnosis and adapting to different sequencing modalities. Materials and Methods: This study included 146 patients diagnosed with cancer and 122 non-cancer controls. EM-DeepSD comprises three core modules. Initially, it utilizes a signal decomposition module to decompose and reconstruct the input end-motif profiles, thereby generating multiple regular subsequences that optimize the subsequent modeling process. Subsequently, both a machine learning module and a deep learning module are employed to improve the accuracy of cancer diagnosis. Furthermore, this paper compares the performance of EM-DeepSD with that of existing benchmarked methods to demonstrate its superiority. Based on the EM-DeepSD framework, we developed the EM-DeepSSA model and compared it with two benchmarked methods across different cfDNA sequencing datasets. Results: In the internal validation set, EM-DeepSSA outperformed the two benchmark methods for cancer diagnosis (area under the curve (AUC), 0.920; adjusted p value < 0.05). Meanwhile, EM-DeepSSA also exhibited the best performance on two independent external testing sets that were subjected to 5-hydroxymethylcytosine sequencing (5hmCS) and broad-range cell-free DNA sequencing (BR-cfDNA-Seq), respectively (test set-1: AUC = 0.933; test set-2: AUC = 0.956; adjusted p value < 0.05). Conclusions: In summary, we present a new framework which can achieve high classification performance in cancer diagnosis and which is applicable to different sequencing modalities. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Signal Analysis)
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17 pages, 394 KiB  
Article
Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features
by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich and Milan Toma
Diagnostics 2025, 15(7), 903; https://doi.org/10.3390/diagnostics15070903 - 1 Apr 2025
Cited by 1 | Viewed by 411
Abstract
Background/Objectives: Electrocardiogram data are widely used to diagnose cardiovascular diseases, a leading cause of death globally. Traditional interpretation methods are manual, time-consuming, and prone to error. Machine learning offers a promising alternative for automating the classification of electrocardiogram abnormalities. This study explores the [...] Read more.
Background/Objectives: Electrocardiogram data are widely used to diagnose cardiovascular diseases, a leading cause of death globally. Traditional interpretation methods are manual, time-consuming, and prone to error. Machine learning offers a promising alternative for automating the classification of electrocardiogram abnormalities. This study explores the use of machine learning models to classify electrocardiogram abnormalities using a dataset that combines clinical features (e.g., age, weight, smoking status) with key electrocardiogram measurements, without relying on time-series data. Methods: The dataset included demographic and electrocardiogram-related biometric data. Preprocessing steps addressed class imbalance, outliers, feature scaling, and the encoding of categorical variables. Five machine learning models—Gaussian Naive Bayes, support vector machines, random forest trees, extremely randomized trees, gradient boosted trees, and an ensemble of top-performing classifiers—were trained and optimized using stratified k-fold cross-validation. Model performance was evaluated on a reserved testing set using metrics such as accuracy, precision, recall, and F1-score. Results: The extremely randomized trees model achieved the best performance, with a testing accuracy of 66.79%, recall of 66.79%, and F1-score of 62.93%. Ventricular rate, QRS duration, and QTC (Bezet) were identified as the most important features. Challenges in classifying borderline cases were noted due to class imbalance and overlapping features. Conclusions: This study demonstrates the potential of machine learning models, particularly extremely randomized trees, in classifying electrocardiogram abnormalities using demographic and biometric data. While promising, the absence of time-series data limits diagnostic accuracy. Future work incorporating time-series signals and advanced deep learning techniques could further improve performance and clinical relevance. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Signal Analysis)
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