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Advanced Machine Intelligence for Biomedical Signal Processing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 24931

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Special Issue Editors

Dr. Hasan Ogul

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Guest Editor

Faculty of Computer Science, Østfold University College, 1757 Halden, Norway
Interests: computational intelligence with applications in health informatics; bioinformatics; text mining and image/video understanding

Dr. Suzan Arslanturk

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Guest Editor

Department of Computer Science, Wayne State University, 5057 Woodward Ave., Detroit, MI 48202, USA
Interests: predictive analytics; machine learning; medical informatics; healthcare systems

Special Issue Information

Dear Colleagues,

Following the recent advances in novel signal processing techniques and methodologies for diagnoses, therapy, monitoring, and rehabilitation, the current medical environment is undergoing great changes. Remarkable progress has been made toward solving practical problems in many fields, such as medicine, digital health, brain science, human–computer interfaces, robots, and biometrics. At the same time, the extensive application of artificial intelligence, big data, and machine learning in the medical field has attracted increasing attention to the design and development of automatic analysis systems.

Compared to the traditional signal processing techniques, machine intelligence, which includes artificial neural networks, pattern recognition, random forest, support vector machines, deep learning, and so on, has proved its effectiveness in solving difficult and complex problems related to biomedical signal processing, analysis, modeling, and classification. Although the current research in this field has shown promising results, several research issues, such as feature selection, class imbalance, and predictive performance still need to be further explored.

This Special Issue focuses on advanced research regarding the potential applications of advanced machine intelligence in biomedical signal processing. Researchers are invited to present original research and their latest findings related to the current trends and challenges in biomedical signal processing based on the algorithms and techniques of artificial intelligence. Topics of interest include, but are not limited to, the following:

EEG/EMG/EOG/PPG analysis.
Biomedical data and signal acquisition.
Machine intelligence for biomedical data analysis, modeling, and classification.
Machine intelligence for medical image analysis, modeling, and classification.
Big data analytics for biomedical applications.
Biomedical applications in physiology, motion control, human–computer interfaces, etc.
Machine Intelligence for personalized medicine.

Dr. Hasan Ogul
Dr. Suzan Arslanturk
Guest Editors

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Published Papers (9 papers)

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Research

16 pages, 4301 KiB

Open AccessArticle

Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting

by Sina Shafiezadeh, Gian Marco Duma, Giovanni Mento, Alberto Danieli, Lisa Antoniazzi, Fiorella Del Popolo Cristaldi, Paolo Bonanni and Alberto Testolin

Sensors 2024, 24(9), 2863; https://doi.org/10.3390/s24092863 - 30 Apr 2024

Viewed by 1239

Abstract

The recent scientific literature abounds in proposals of seizure forecasting methods that exploit machine learning to automatically analyze electroencephalogram (EEG) signals. Deep learning algorithms seem to achieve a particularly remarkable performance, suggesting that the implementation of clinical devices for seizure prediction might be within reach. However, most of the research evaluated the robustness of automatic forecasting methods through randomized cross-validation techniques, while clinical applications require much more stringent validation based on patient-independent testing. In this study, we show that automatic seizure forecasting can be performed, to some extent, even on independent patients who have never been seen during the training phase, thanks to the implementation of a simple calibration pipeline that can fine-tune deep learning models, even on a single epileptic event recorded from a new patient. We evaluate our calibration procedure using two datasets containing EEG signals recorded from a large cohort of epileptic subjects, demonstrating that the forecast accuracy of deep learning methods can increase on average by more than 20%, and that performance improves systematically in all independent patients. We further show that our calibration procedure works best for deep learning models, but can also be successfully applied to machine learning algorithms based on engineered signal features. Although our method still requires at least one epileptic event per patient to calibrate the forecasting model, we conclude that focusing on realistic validation methods allows to more reliably compare different machine learning approaches for seizure prediction, enabling the implementation of robust and effective forecasting systems that can be used in daily healthcare practice. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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16 pages, 1641 KiB

Open AccessArticle

Energy Expenditure Prediction from Accelerometry Data Using Long Short-Term Memory Recurrent Neural Networks

by Martin Vibæk, Abdolrahman Peimankar, Uffe Kock Wiil, Daniel Arvidsson and Jan Christian Brønd

Sensors 2024, 24(8), 2520; https://doi.org/10.3390/s24082520 - 14 Apr 2024

Viewed by 1297

Abstract

The accurate estimation of energy expenditure from simple objective accelerometry measurements provides a valuable method for investigating the effect of physical activity (PA) interventions or population surveillance. Methods have been evaluated previously, but none utilize the temporal aspects of the accelerometry data. In this study, we investigated the energy expenditure prediction from acceleration measured at the subjects’ hip, wrist, thigh, and back using recurrent neural networks utilizing temporal elements of the data. The acceleration was measured in children (N = 33) performing a standardized activity protocol in their natural environment. The energy expenditure was modelled using Multiple Linear Regression (MLR), stacked long short-term memory (LSTM) networks, and combined convolutional neural networks (CNN) and LSTM. The correlation and mean absolute percentage error (MAPE) were 0.76 and 19.9% for the MLR, 0.882 and 0.879 and 14.22% for the LSTM, and, with the combined LSTM-CNN, the best performance of 0.883 and 13.9% was achieved. The prediction error for vigorous intensities was significantly different (p < 0.01) from those of the other intensity domains: sedentary, light, and moderate. Utilizing the temporal elements of movement significantly improves energy expenditure prediction accuracy compared to other conventional approaches, but the prediction error for vigorous intensities requires further investigation. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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14 pages, 616 KiB

Open AccessArticle

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models

by Joo Hun Yoo, Harim Jeong, Ji Hyun An and Tai-Myoung Chung

Sensors 2024, 24(2), 715; https://doi.org/10.3390/s24020715 - 22 Jan 2024

Cited by 3 | Viewed by 2202

Abstract

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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23 pages, 1282 KiB

Open AccessArticle

Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers

by Loredana Daria Mang, Francisco David González Martínez, Damian Martinez Muñoz, Sebastián García Galán and Raquel Cortina

Sensors 2024, 24(2), 682; https://doi.org/10.3390/s24020682 - 21 Jan 2024

Cited by 2 | Viewed by 2245

Abstract

Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system’s condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer (ViT) architecture, since this input–classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self-attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state-of-the-art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant-Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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17 pages, 2131 KiB

Open AccessArticle

k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation

by Jacob White and Sarah D. Power

Sensors 2023, 23(13), 6077; https://doi.org/10.3390/s23136077 - 1 Jul 2023

Cited by 10 | Viewed by 3015

Abstract

In passive BCI studies, a common approach is to collect data from mental states of interest during relatively long trials and divide these trials into shorter “epochs” to serve as individual samples in classification. While it is known that using k-fold cross-validation (CV) in this scenario can result in unreliable estimates of mental state separability (due to autocorrelation in the samples derived from the same trial), k-fold CV is still commonly used and reported in passive BCI studies. What is not known is the extent to which k-fold CV misrepresents true mental state separability. This makes it difficult to interpret the results of studies that use it. Furthermore, if the seriousness of the problem were clearly known, perhaps more researchers would be aware that they should avoid it. In this work, a novel experiment explored how the degree of correlation among samples within a class affects EEG-based mental state classification accuracy estimated by k-fold CV. Results were compared to a ground-truth (GT) accuracy and to “block-wise” CV, an alternative to k-fold which is purported to alleviate the autocorrelation issues. Factors such as the degree of true class separability and the feature set and classifier used were also explored. The results show that, under some conditions, k-fold CV inflated the GT classification accuracy by up to 25%, but block-wise CV underestimated the GT accuracy by as much as 11%. It is our recommendation that the number of samples derived from the same trial should be reduced whenever possible in single-subject analysis, and that both the k-fold and block-wise CV results are reported. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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34 pages, 2575 KiB

Open AccessArticle

Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients

by Sofía Martín-González, Antonio G. Ravelo-García, Juan L. Navarro-Mesa and Eduardo Hernández-Pérez

Sensors 2023, 23(9), 4267; https://doi.org/10.3390/s23094267 - 25 Apr 2023

Cited by 1 | Viewed by 2440

Abstract

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO₂, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO₂-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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16 pages, 2979 KiB

Open AccessArticle

Diverse Task Classification from Activation Patterns of Functional Neuro-Images Using Feature Fusion Module

by Osman Tayfun Bişkin, Cemre Candemir, Ali Saffet Gonul and Mustafa Alper Selver

Sensors 2023, 23(7), 3382; https://doi.org/10.3390/s23073382 - 23 Mar 2023

Cited by 2 | Viewed by 1813

Abstract

One of the emerging fields in functional magnetic resonance imaging (fMRI) is the decoding of different stimulations. The underlying idea is to reveal the hidden representative signal patterns of various fMRI tasks for achieving high task-classification performance. Unfortunately, when multiple tasks are processed, performance remains limited due to several challenges, which are rarely addressed since the majority of the state-of-the-art studies cover a single neuronal activity task. Accordingly, the first contribution of this study is the collection and release of a rigorously acquired dataset, which contains cognitive, behavioral, and affective fMRI tasks together with resting state. After a comprehensive analysis of the pitfalls of existing systems on this new dataset, we propose an automatic multitask classification (MTC) strategy using a feature fusion module (FFM). FFM aims to create a unique signature for each task by combining deep features with time-frequency representations. We show that FFM creates a feature space that is superior for representing task characteristics compared to their individual use. Finally, for MTC, we test a diverse set of deep-models and analyze their complementarity. Our results reveal higher classification accuracy compared to benchmarks. Both the dataset and the code are accessible to researchers for further developments. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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28 pages, 2963 KiB

Open AccessArticle

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

by Erkan Bostanci, Engin Kocak, Metehan Unal, Mehmet Serdar Guzel, Koray Acici and Tunc Asuroglu

Sensors 2023, 23(6), 3080; https://doi.org/10.3390/s23063080 - 13 Mar 2023

Cited by 10 | Viewed by 6636

Abstract

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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16 pages, 1559 KiB

Open AccessArticle

Morphological Autoencoders for Beat-by-Beat Atrial Fibrillation Detection Using Single-Lead ECG

by Rafael Silva, Ana Fred and Hugo Plácido da Silva

Sensors 2023, 23(5), 2854; https://doi.org/10.3390/s23052854 - 6 Mar 2023

Cited by 3 | Viewed by 2439

Abstract

Engineered feature extraction can compromise the ability of Atrial Fibrillation (AFib) detection algorithms to deliver near real-time results. Autoencoders (AEs) can be used as an automatic feature extraction tool, tailoring the resulting features to a specific classification task. By coupling an encoder to a classifier, it is possible to reduce the dimension of the Electrocardiogram (ECG) heartbeat waveforms and classify them. In this work we show that morphological features extracted using a Sparse AE are sufficient to distinguish AFib from Normal Sinus Rhythm (NSR) beats. In addition to the morphological features, rhythm information was included in the model using a proposed short-term feature called Local Change of Successive Differences (LCSD). Using single-lead ECG recordings from two referenced public databases, and with features from the AE, the model was able to achieve an F1-score of 88.8%. These results show that morphological features appear to be a distinct and sufficient factor for detecting AFib in ECG recordings, especially when designed for patient-specific applications. This is an advantage over state-of-the-art algorithms that need longer acquisition times to extract engineered rhythm features, which also requires careful preprocessing steps. To the best of our knowledge, this is the first work that presents a near real-time morphological approach for AFib detection under naturalistic ECG acquisition with a mobile device. Full article

(This article belongs to the Special Issue Advanced Machine Intelligence for Biomedical Signal Processing)

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