Long-Term Health Monitoring with Physiological Signals - Volume 2

Topic Information

Dear Colleagues,

The human body constantly produces physiological signals such as heat and electrical impulses from the muscles, brain, and other organs. Since the dawn of modern medicine, these signals have been used to assess and, in some cases, determine the cause of a health crisis. From these ancient roots, modern medicine has grown and diversified. However, the idea of crisis is still preserved. Like Hippocrates of Kos, modern medicine springs into action when a health crisis occurs. This event-driven setup is very resource efficient because health services are only used if there is reason to do so. However, sometimes a disease might have progressed beyond a point where effective treatment is available before symptoms trigger a diagnosis. In the past, resource efficiency by far outweighed the potential benefits of continuous physiological signal monitoring. However, in recent years, technological advances have meant that communication, storage, and processing resources have become almost omnipresent at a competitive price point. Having recognized the transformative nature of this technology, for this topic, entitled “Long-Term Health Monitoring with Physiological Signals”, we seek answers to the question: how can we use physiological signal measurements to translate the resource abundance into improved outcomes for patients? We invite papers that recognize the potential of gathering and analysing big physiological data for possible publication in one of the five journals: Diagnostics, Life, Healthcare, BioMed, or Signals. A possible application area is the creation of disease-specific solutions where physiological signals are analysed with advanced artificial intelligence algorithms. Examples include atrial fibrillation detection and sleep monitoring in the home environment. Another area of interest is long-term physiological signal analysis for rehabilitation tracking and geriatric care.

Prof. Dr. U Rajendra Acharya
Dr. Oliver Faust
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
BioMed biomed	-	-	2021	27 Days	CHF 1000
Diagnostics diagnostics	3.6	3.6	2011	20.7 Days	CHF 2600
Healthcare healthcare	2.8	2.7	2013	19.5 Days	CHF 2700
Life life	3.2	2.7	2011	17.5 Days	CHF 2600
Signals signals	-	-	2020	35.1 Days	CHF 1000

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Published Papers (6 papers)

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Open AccessBrief Report

Multifractal Heart Rate Value Analysis: A Novel Approach for Diabetic Neuropathy Diagnosis

by Andrea Coppola, Sergio Conte, Donatella Pastore, Francesca Chiereghin and Giulia Donadel

Healthcare 2024, 12(2), 234; https://doi.org/10.3390/healthcare12020234 - 17 Jan 2024

Viewed by 583

Abstract

Type 2 diabetes mellitus (T2DM) is characterized by several complications, such as retinopathy, renal failure, cardiovascular disease, and diabetic neuropathy. Among these, neuropathy is the most severe complication, due to the challenging nature of its early detection. The linear Hearth Rate Variability (HRV) [...] Read more.

Type 2 diabetes mellitus (T2DM) is characterized by several complications, such as retinopathy, renal failure, cardiovascular disease, and diabetic neuropathy. Among these, neuropathy is the most severe complication, due to the challenging nature of its early detection. The linear Hearth Rate Variability (HRV) analysis is the most common diagnosis technique for diabetic neuropathy, and it is characterized by the determination of the sympathetic–parasympathetic balance on the peripheral nerves through a linear analysis of the tachogram obtained using photoplethysmography. We aimed to perform a multifractal analysis to identify autonomic neuropathy, which was not yet manifest and not detectable with the linear HRV analysis. We enrolled 10 healthy controls, 10 T2DM-diagnosed patients with not-full-blown neuropathy, and 10 T2DM diagnosed patients with full-blown neuropathy. The tachograms for the HRV analysis were obtained using finger photoplethysmography and a linear and/or multifractal analysis was performed. Our preliminary results showed that the linear analysis could effectively differentiate between healthy patients and T2DM patients with full-blown neuropathy; nevertheless, no differences were revealed comparing the full-blown to not-full-blown neuropathic diabetic patients. Conversely, the multifractal HRV analysis was effective for discriminating between full-blown and not-full-blown neuropathic T2DM patients. The multifractal analysis can represent a powerful strategy to determine neuropathic onset, even without clinical diagnostic evidence. Full article

(This article belongs to the Topic Long-Term Health Monitoring with Physiological Signals - Volume 2)

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13 pages, 1825 KiB  

Open AccessArticle

Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification

by Zeynep Ozpolat and Murat Karabatak

Diagnostics 2023, 13(6), 1099; https://doi.org/10.3390/diagnostics13061099 - 14 Mar 2023

Cited by 6 | Viewed by 2288

Abstract

The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, [...] Read more.

The electrocardiogram (ECG) is the most common technique used to diagnose heart diseases. The electrical signals produced by the heart are recorded by chest electrodes and by the extremity electrodes placed on the limbs. Many diseases, such as arrhythmia, cardiomyopathy, coronary heart disease, and heart failure, can be diagnosed by examining ECG signals. The interpretation of these signals by experts may take a long time, and there may be differences between expert interpretations. Since technological developments are intertwined with the medical sciences, computer-assisted diagnostic methods have recently come forward. In computer science, machine learning techniques are often preferred for automatic detection. Quantum-based structures have emerged to increase the machine learning algorithm’s speed and classification performance. In this study, a quantum-based machine learning algorithm is applied to classify heart rhythms. The ECG properties were converted to qubit structure using principal component analysis (PCA). The resulting qubits are classified using the quantum support vector machine (QSVM) algorithm. Quantum computer simulation over Qiskit was used for classification studies. Within the scope of experimental studies, comparisons between classical SVM and QSVM were made using different data amounts and qubit numbers. In the results of the analysis, classical SVM achieved 86.96% accuracy, and QSVM achieved 84.64% accuracy. Despite the fact that the entire dataset was not used due to various limitations, these successful performances were achieved. Classification of medical data such as that from ECG has shown that quantum-based machine learning frameworks perform well despite current resource constraints. In this respect, the study includes essential contributions to the use of quantum-based machine learning methods on signal data in medicine. Full article

(This article belongs to the Topic Long-Term Health Monitoring with Physiological Signals - Volume 2)

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20 pages, 4007 KiB  

Open AccessArticle

Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics

by Hilda Mayrose, G. Muralidhar Bairy, Niranjana Sampathila, Sushma Belurkar and Kavitha Saravu

Diagnostics 2023, 13(2), 220; https://doi.org/10.3390/diagnostics13020220 - 06 Jan 2023

Cited by 7 | Viewed by 7159

Abstract

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, [...] Read more.

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, labor-intensive, and not affordable to common people. This paper describes a method that can support clinicians during dengue diagnosis. It is proposed to automate the peripheral blood smear (PBS) examination using Artificial Intelligence (AI) to aid dengue diagnosis. Nowadays, AI, especially Machine Learning (ML), is increasingly being explored for successful analyses in the biomedical field. Digital pathology coupled with AI holds great potential in developing healthcare services. The automation system developed incorporates a blob detection method to detect platelets and thrombocytopenia from the PBS images. The results achieved are clinically acceptable. Moreover, an ML-based technique is proposed to detect dengue from the images of PBS based on the lymphocyte nucleus. Ten features are extracted, including six morphological and four Gray Level Spatial Dependance Matrix (GLSDM) features, out of the lymphocyte nucleus of normal and dengue cases. Features are then subjected to various popular supervised classifiers built using a ten-fold cross-validation policy for automated dengue detection. Among all the classifiers, the best performance was achieved by Support Vector Machine (SVM) and Decision Tree (DT), each with an accuracy of 93.62%. Furthermore, 1000 deep features extracted using pre-trained MobileNetV2 and 177 textural features extracted using Local binary pattern (LBP) from the lymphocyte nucleus are subjected to feature selection. The ReliefF selected 100 most significant features are then fed to the classifiers. The best performance was attained using an SVM classifier with 95.74% accuracy. With the obtained results, it is evident that this proposed approach can efficiently contribute as an adjuvant tool for diagnosing dengue from the digital microscopic images of PBS. Full article

(This article belongs to the Topic Long-Term Health Monitoring with Physiological Signals - Volume 2)

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18 pages, 5364 KiB  

Open AccessArticle

Significance of Features from Biomedical Signals in Heart Health Monitoring

by Mohammad Mahbubur Rahman Khan Mamun

BioMed 2022, 2(4), 391-408; https://doi.org/10.3390/biomed2040031 - 10 Nov 2022

Cited by 2 | Viewed by 2435

Abstract

Cardiovascular diseases require extensive diagnostic tests and frequent physician visits. With the advance in signal processing and sensor technology, now it is possible to acquire vital signs from the human body and process the signal to extract features necessary to primarily diagnose symptoms [...] Read more.

Cardiovascular diseases require extensive diagnostic tests and frequent physician visits. With the advance in signal processing and sensor technology, now it is possible to acquire vital signs from the human body and process the signal to extract features necessary to primarily diagnose symptoms of cardiovascular disease early. This can help prevent deadly health incidents such as heart attack and or stroke, as well as reduce the number of visits to a health care facility. The proper detection of an elevated ST segment of ECG wave at an early stage may save the patient from having a heart attack or ST elevated myocardial infarction later. The use of a variety of complementary biomedical sensors can lead to a better diagnosis than what is possible when a single sensor is used. This paper proposes a MATLAB GUI which can detect elevated ST segments of ECG waves and use information from a variety of biomedical sensors to bring forth a technique to assess heart health to predict potential heart failure conditions. The proposed technique used fusion among multiple biomedical sensors to reduce the false alarm in diagnosis. Data from the online dataset were used to show the effectiveness and promise of the proposed detection of elevated ST segments and diagnosis techniques using the GUI. Full article

(This article belongs to the Topic Long-Term Health Monitoring with Physiological Signals - Volume 2)

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20 pages, 2290 KiB  

Open AccessArticle

L-Tetrolet Pattern-Based Sleep Stage Classification Model Using Balanced EEG Datasets

by Prabal Datta Barua, Ilknur Tuncer, Emrah Aydemir, Oliver Faust, Subrata Chakraborty, Vinithasree Subbhuraam, Turker Tuncer, Sengul Dogan and U. Rajendra Acharya

Diagnostics 2022, 12(10), 2510; https://doi.org/10.3390/diagnostics12102510 - 16 Oct 2022

Cited by 3 | Viewed by 1909

Abstract

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the [...] Read more.

Background: Sleep stage classification is a crucial process for the diagnosis of sleep or sleep-related diseases. Currently, this process is based on manual electroencephalogram (EEG) analysis, which is resource-intensive and error-prone. Various machine learning models have been recommended to standardize and automate the analysis process to address these problems. Materials and methods: The well-known cyclic alternating pattern (CAP) sleep dataset is used to train and test an L-tetrolet pattern-based sleep stage classification model in this research. By using this dataset, the following three cases are created, and they are: Insomnia, Normal, and Fused cases. For each of these cases, the machine learning model is tasked with identifying six sleep stages. The model is structured in terms of feature generation, feature selection, and classification. Feature generation is established with a new L-tetrolet (Tetris letter) function and multiple pooling decomposition for level creation. We fuse ReliefF and iterative neighborhood component analysis (INCA) feature selection using a threshold value. The hybrid and iterative feature selectors are named threshold selection-based ReliefF and INCA (TSRFINCA). The selected features are classified using a cubic support vector machine. Results: The presented L-tetrolet pattern and TSRFINCA-based sleep stage classification model yield 95.43%, 91.05%, and 92.31% accuracies for Insomnia, Normal dataset, and Fused cases, respectively. Conclusion: The recommended L-tetrolet pattern and TSRFINCA-based model push the envelope of current knowledge engineering by accurately classifying sleep stages even in the presence of sleep disorders. Full article

(This article belongs to the Topic Long-Term Health Monitoring with Physiological Signals - Volume 2)

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14 pages, 4586 KiB  

Open AccessArticle

Use of Differential Entropy for Automated Emotion Recognition in a Virtual Reality Environment with EEG Signals

by Hakan Uyanık, Salih Taha A. Ozcelik, Zeynep Bala Duranay, Abdulkadir Sengur and U. Rajendra Acharya

Diagnostics 2022, 12(10), 2508; https://doi.org/10.3390/diagnostics12102508 - 16 Oct 2022

Cited by 6 | Viewed by 2077

Abstract

Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better [...] Read more.

Emotion recognition is one of the most important issues in human–computer interaction (HCI), neuroscience, and psychology fields. It is generally accepted that emotion recognition with neural data such as electroencephalography (EEG) signals, functional magnetic resonance imaging (fMRI), and near-infrared spectroscopy (NIRS) is better than other emotion detection methods such as speech, mimics, body language, facial expressions, etc., in terms of reliability and accuracy. In particular, EEG signals are bioelectrical signals that are frequently used because of the many advantages they offer in the field of emotion recognition. This study proposes an improved approach for EEG-based emotion recognition on a publicly available newly published dataset, VREED. Differential entropy (DE) features were extracted from four wavebands (theta 4–8 Hz, alpha 8–13 Hz, beta 13–30 Hz, and gamma 30–49 Hz) to classify two emotional states (positive/negative). Five classifiers, namely Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Naïve Bayesian (NB), Decision Tree (DT), and Logistic Regression (LR) were employed with DE features for the automated classification of two emotional states. In this work, we obtained the best average accuracy of 76.22% ± 2.06 with the SVM classifier in the classification of two states. Moreover, we observed from the results that the highest average accuracy score was produced with the gamma band, as previously reported in studies in EEG-based emotion recognition. Full article

(This article belongs to the Topic Long-Term Health Monitoring with Physiological Signals - Volume 2)

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