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Physiological Signal Analysis Methods in Healthcare
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Physiological Signal Analysis Methods in Healthcare

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (10 March 2023) | Viewed by 13607

Special Issue Editors

Prof. Dr. Hsien-Tsai Wu

E-Mail Website
Guest Editor
Department of Electrical Engineering, National Dong-Hwa University, Hualien 97401, Taiwan
Interests: medical data analysis; multiscale entropy; photoplethysmograph applications; diabetes and prognostic indicators
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Cheuk-Kwan Sun

E-Mail Website
Co-Guest Editor
Department of Emergency Medicine, E-Da Hospital, Kaohsiung City 80708, Taiwan
Interests: hepatobiliary and alimentary tract surgeries; endocrine and breast surgeries; cardiovascular and circulatory physiology; translational research in medicine; stem cell medicine; quality improvement in patient care; translational medicine; physiological signals; cardiovascular physics
Prof. Dr. Jian-Jung Chen

E-Mail Website
Co-Guest Editor
1. School of Post-Baccalaureate Chinese Medicine, Tzu Chi University, Hualien 97002, Taiwan
2. Taichung Tzuchi Hospital, The Buddhist Tzuchi Medical Foundation, Taichung 42743, Taiwan
Interests: pulse diagnosis; Chinese medicine, translational research in medicine; quality improvement in patient care

Special Issue Information

Dear Colleagues, 

With the advances in medical technology and pharmacology in recent decades, the aged population is increasing worldwide. When combined with the Westernized lifestyle of developed countries, this gives rise to chronic diseases such as hypertension and diabetes mellitus that have been reported to contribute to the development of atherosclerosis and dysfunction of the autonomic nervous system, thereby leading to more severe and even life-threatening cardiovascular diseases. On the other hand, serious though they seem, there are no obvious symptoms suggestive of cardiovascular or autonomic nervous diseases in their early stages. Early detection of the signs of these potentially fatal diseases through signal analysis methods, therefore, is of utmost importance in the field of preventive medicine and requires the collaborative effort of clinicians and medical technologists.

In the clinical domain, physiological signal analysis methods aiming at extracting hidden information from medical data are incessantly being explored. They are now commonly used for research activities or in clinical routines for the diagnosis or treatment of various disease entities. In addition to the requirement for the development of new methods in an attempt to improve the cost-effectiveness and accuracy of the existing ones, new applications for new and existing methods also keep emerging. Despite the apparently remote relationship between bioengineering and medicine, the development of the former has begun to unlock the secrets hidden inside different physiological signals through which clinicians could follow the progression or even predict the outcome of a variety of diseases.

This Special Issue is intended to present and discuss physiological signal analysis methods in healthcare (linear/nonlinear analysis) and their applications. It also aims at facilitating the exchange of ideas and promoting interactions between investigators across different specialties. To emphasize the comprehensiveness of the topic, papers focusing on signal analysis ranging from organ to system level in both physiological and pathological situations are anticipated. We sincerely invite investigators to contribute to this Special Issue by submitting reviews and original papers.

Potential topics include, but are not limited to:

  • Linear and nonlinear analysis of physiological time series
  • Methods in diagnosis or treatment optimization
  • Diagnostic decision support systems
  • Comprehensive and intensive computing in healthcare information systems
  • Visualization methods for healthcare big data
  • Artificial intelligence for cardiovascular disease diagnosis
  • Computation in healthy aging for nursing home residents
  • Data analysis for hypertension and coronary sclerosis
  • Data analysis for adult diseases
  • Data analysis in personalized and precision medicine
  • Medical image analysis for cardiovascular disease diagnosis
  • Data analysis for wearable devices
  • Traditional Chinese therapeutic approaches

Prof. Dr. Hsien-Tsai Wu
Prof. Dr. Cheuk-Kwan Sun
Prof. Dr. Jian-Jung Chen
Guest Editors

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. Journal of Personalized Medicine is an international peer-reviewed open access monthly 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

  • disease and prognostic indicators
  • physiological time series
  • medical data analysis in healthcare
  • bioengineering in healthcare
  • cardiovascular relative disease diagnosis
  • medical technology
  • traditional chinese therapeutic approaches

Published Papers (5 papers)

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Research

24 pages, 7769 KiB  
Article
Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model
by Yating Hu, Tengfei Feng, Miao Wang, Chengyu Liu and Hong Tang
J. Pers. Med. 2023, 13(5), 820; https://doi.org/10.3390/jpm13050820 - 12 May 2023
Cited by 3 | Viewed by 1912
Abstract
Background and Objectives: Atrial fibrillation (AF) is one of the most common arrhythmias clinically. Aging tends to increase the risk of AF, which also increases the burden of other comorbidities, including coronary artery disease (CAD), and even heart failure (HF). The precise detection [...] Read more.
Background and Objectives: Atrial fibrillation (AF) is one of the most common arrhythmias clinically. Aging tends to increase the risk of AF, which also increases the burden of other comorbidities, including coronary artery disease (CAD), and even heart failure (HF). The precise detection of AF is a challenge due to its intermittence and unpredictability. A method for the accurate detection of AF is still needed. Methods: A deep learning model was used to detect atrial fibrillation. Here, a distinction was not made between AF and atrial flutter (AFL), both of which manifest as a similar pattern on an electrocardiogram (ECG). This method not only discriminated AF from normal rhythm of the heart, but also detected its onset and offset. The proposed model involved residual blocks and a Transformer encoder. Results and Conclusions: The data used for training were obtained from the CPSC2021 Challenge, and were collected using dynamic ECG devices. Tests on four public datasets validated the availability of the proposed method. The best performance for AF rhythm testing attained an accuracy of 98.67%, a sensitivity of 87.69%, and a specificity of 98.56%. In onset and offset detection, it obtained a sensitivity of 95.90% and 87.70%, respectively. The algorithm with a low FPR of 0.46% was able to reduce troubling false alarms. The model had a great capability to discriminate AF from normal rhythm and to detect its onset and offset. Noise stress tests were conducted after mixing three types of noise. We visualized the model’s features using a heatmap and illustrated its interpretability. The model focused directly on the crucial ECG waveform where showed obvious characteristics of AF. Full article
(This article belongs to the Special Issue Physiological Signal Analysis Methods in Healthcare)
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17 pages, 5418 KiB  
Article
Development of a Multi-Channel Wearable Heart Sound Visualization System
by Binbin Guo, Hong Tang, Shufeng Xia, Miao Wang, Yating Hu and Zehang Zhao
J. Pers. Med. 2022, 12(12), 2011; https://doi.org/10.3390/jpm12122011 - 04 Dec 2022
Cited by 4 | Viewed by 3420
Abstract
A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart [...] Read more.
A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart sound signals and one ECG signal simultaneously using 72 heart sound sensors placed on the chest surface and one ECG analog front end. The novel heart sound sensors had the advantages of high signal quality, small size, and high sensitivity. Butterworth filtering and wavelet transform were used to reduce noise in the signals. The cardiac acoustic map was obtained based on the cubic spline interpolation of the heart sound signals. The results showed the heart sound signals on the chest surface could be detected and visualized by this system. The variations of heart sounds were clearly displayed. This study provided a way to select optimal position for auscultation of heart sounds. The visualization system could provide a technology for investigating the propagation of heart sound in the thoracic cavity. Full article
(This article belongs to the Special Issue Physiological Signal Analysis Methods in Healthcare)
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Graphical abstract

12 pages, 1323 KiB  
Article
Calculation of an Improved Stiffness Index Using Decomposed Radial Pulse and Digital Volume Pulse Signals
by Hsien-Tsai Wu and Jian-Jung Chen
J. Pers. Med. 2022, 12(11), 1768; https://doi.org/10.3390/jpm12111768 - 26 Oct 2022
Cited by 1 | Viewed by 2264
Abstract
The stiffness index (SI) is used to estimate cardiovascular risk in humans. In this study, we developed a refined SI for determining arterial stiffness based on the decomposed radial pulse and digital volume pulse (DVP) waveforms. In total, 40 mature asymptomatic subjects (20 [...] Read more.
The stiffness index (SI) is used to estimate cardiovascular risk in humans. In this study, we developed a refined SI for determining arterial stiffness based on the decomposed radial pulse and digital volume pulse (DVP) waveforms. In total, 40 mature asymptomatic subjects (20 male and 20 female, 42 to 76 years of age) and 40 subjects with type 2 diabetes mellitus (T2DM) (23 male and 17 female, 35 to 78 years of age) were enrolled in this study. We measured subjects’ radial pulse at the wrist and their DVP at the fingertip, and then implemented ensemble empirical mode decomposition (EEMD) to derive the orthogonal intrinsic mode functions (IMFs). An improved SI (SInew) was calculated by dividing the body height by the mean transit time between the first IMF5 peak and the IMF6 trough. Another traditional index, pulse wave velocity (PWVfinger), was also included for comparison. For the PWVfinger index, the subjects with T2DM presented significantly higher SInew values measured according to the radial pulse (SInew-RP) and DVP signals (SInew-DVP). Using a one-way analysis of variance, we found no statistically significant difference between SInew-RP and PWVfinger when applied to the same test subjects. Binary logistic regression analysis showed that a high SInew-RP value was the most significant risk factor for developing T2DM (SInew-RP odds ratio 3.17, 95% CI 1.53–6.57; SInew-DVP odds ratio 2.85, 95% CI 1.27–6.40). Our refined stiffness index could provide significant information regarding the decomposed radial pulse and digital volume pulse signals in assessments of arterial stiffness. Full article
(This article belongs to the Special Issue Physiological Signal Analysis Methods in Healthcare)
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17 pages, 22079 KiB  
Article
Identification of Patients with Potential Atrial Fibrillation during Sinus Rhythm Using Isolated P Wave Characteristics from 12-Lead ECGs
by Hui-Wen Yang, Cheng-Yi Hsiao, Yu-Qi Peng, Tse-Yu Lin, Lung-Wen Tsai, Chen Lin, Men-Tzung Lo and Chun-Ming Shih
J. Pers. Med. 2022, 12(10), 1608; https://doi.org/10.3390/jpm12101608 - 29 Sep 2022
Cited by 3 | Viewed by 1862
Abstract
Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size [...] Read more.
Atrial fibrillation (AF) is largely underdiagnosed. Previous studies using deep neural networks with large datasets have shown that screening AF with a 12-lead electrocardiogram (ECG) during sinus rhythm (SR) is possible. However, the poor availability of these trained models and the small size of the retrievable datasets limit its reproducibility. This study proposes an approach to generate explainable features for detecting AF during SR with limited data. We collected 94,224 12-lead ECGs from 64,196 patients from Taipei Medical University Hospital. We selected ECGs during SR from 213 patients before AF diagnosis and randomly selected 247 age-matched participants without AF records as the controls. We developed a signal-processing technique, MA-UPEMD, to isolate P waves, and quantified the spatial and temporal features using principal component analysis and inter-lead relationships. By combining these features, the machine learning models yielded AUC of 0.64. We showed that, even with this limited dataset, the P wave, representing atrial electrical activity, is depicted by our proposed approach. The extracted features performed better than the bandpass filter-extracted P waves and deep neural network model. We provided a physiologically explainable and reproducible approach for classifying patients with AF during SR. Full article
(This article belongs to the Special Issue Physiological Signal Analysis Methods in Healthcare)
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14 pages, 2535 KiB  
Article
Wearable Devices and Smartphone Inertial Sensors for Static Balance Assessment: A Concurrent Validity Study in Young Adult Population
by Luciana Abrantes Rodrigues, Enzo Gabriel Rocha Santos, Patrícia Seixas Alves Santos, Yuzo Igarashi, Luana Karine Resende Oliveira, Gustavo Henrique Lima Pinto, Bruno Lopes Santos Lobato, André Santos Cabral, Anderson Belgamo, Anselmo Athayde Costa e Silva, Bianca Callegari and Givago Silva Souza
J. Pers. Med. 2022, 12(7), 1019; https://doi.org/10.3390/jpm12071019 - 21 Jun 2022
Cited by 2 | Viewed by 2403
Abstract
Falls represent a public health issue around the world and prevention is an important part of the politics of many countries. The standard method of evaluating balance is posturography using a force platform, which has high financial costs. Other instruments, such as portable [...] Read more.
Falls represent a public health issue around the world and prevention is an important part of the politics of many countries. The standard method of evaluating balance is posturography using a force platform, which has high financial costs. Other instruments, such as portable devices and smartphones, have been evaluated as low-cost alternatives to the screening of balance control. Although smartphones and wearables have different sizes, shapes, and weights, they have been systematically validated for static balance control tasks. Different studies have applied different experimental configurations to validate the inertial measurements obtained by these devices. We aim to evaluate the concurrent validity of a smartphone and a portable device for the evaluation of static balance control in the same group of participants. Twenty-six healthy and young subjects comprised the sample. The validity for static balance control evaluation of built-in accelerometers inside portable smartphone and wearable devices was tested considering force platform recordings as a gold standard for comparisons. A linear correlation (r) between the quantitative variables obtained from the inertial sensors and the force platform was used as an indicator of the concurrent validity. Reliability of the measures was calculated using Intraclass correlation in a subsample (n = 14). Smartphones had 11 out of 12 variables with significant moderate to very high correlation (r > 0.5, p < 0.05) with force platform variables in open eyes, closed eyes, and unipedal conditions, while wearable devices had 8 out of 12 variables with moderate to very high correlation (r > 0.5, p < 0.05) with force platform variables under the same task conditions. Significant reliabilities were found in closed eye conditions for smartphones and wearables. The smartphone and wearable devices had concurrent validity for the static balance evaluation and the smartphone had better validity results than the wearables for the static balance evaluation. Full article
(This article belongs to the Special Issue Physiological Signal Analysis Methods in Healthcare)
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