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Non-invasive Diagnostics for Cardiovascular Diseases
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Non-invasive Diagnostics for Cardiovascular Diseases

A special issue of Diseases (ISSN 2079-9721). This special issue belongs to the section "Cardiology".

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 23572

Special Issue Editor

Dr. Mohamed Elgendi

E-Mail Website
Guest Editor
Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
Interests: biomedical signal processing; digital medicine; data analysis; health care; machine learning; computer science; psychology and neuroscience; biomedical and health informatics; women's healthcare and wellness

Special Issue Information

Dear Colleagues,

The development of non-invasive cardiovascular disease diagnostics necessitates ongoing knowledge and skills updates to match the current demands and progress in the field. In today’s chaotic world, there is an increasing trend to seek out simple, non-invasive solutions for complex problems that can increase efficiency, reduce resource consumption, and improve scalability. This desire has spilled over into the world of science and research, where many disciplines have taken to investigating and applying more simplistic non-invasive approaches to replace existing invasive technologies that are often costly and require high levels of training. Interestingly, through a review of current literature it seems that invasive diagnostic methods continue to be the gold standard with limited effort towards exploring noninvasive alternatives. Theoretically, a shift towards more simple and non-invasive solutions could yield equal or better results than invasive methods, in addition to needing less resources and training.

The purpose of this Special Issue is to bring together researchers and practitioners from multiple areas of expertise including biology, medicine, engineering and other physical sciences that are interested in studying and using non-invasive techniques for diagnosing diseases. A diversity of non-invasively measured signal types is included in this research area including electrical, image, audio and other biological sources of information. Developing efficient diagnostic tools requires multidisciplinary research that integrates knowledge from medicine (e.g., disease etiology, disease annotation, diagnostic accuracy), engineering (e.g., signal processing, modelling), computer science (e.g., pattern recognition, machine learning, computational intelligence techniques) and other related areas.

Dr. Mohamed Elgendi
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. Diseases 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 1800 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

  • Electrocardiography and Holter Monitoring
  • Photoplethysmogram (Pulse Ox)
  • Heart sounds (cardiac auscultation)
  • Echocardiogram (echo)
  • Chest X-Ray
  • Cardiac Computed Tomography
  • Exercise Stress Test
  • Myocardial perfusion scintigraphy
  • Positron Emission Tomography-PET
  • Coronary angiography CT (Multislice CT Angiography)
  • Cardiac MR

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Published Papers (4 papers)

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Research

14 pages, 1853 KiB  
Article
Innovative Multi-Site Photoplethysmography Analysis for Quantifying Pulse Amplitude and Timing Variability Characteristics in Peripheral Arterial Disease
by Michael Bentham, Gerard Stansby and John Allen
Diseases 2018, 6(3), 81; https://doi.org/10.3390/diseases6030081 - 17 Sep 2018
Cited by 35 | Viewed by 6276
Abstract
Photoplethysmography (PPG) is a simple-to-perform vascular optics measurement technique that can detect blood volume changes in the microvascular bed of tissue. Beat-to-beat analysis of the PPG waveform enables the study of the variability of pulse features, such as the amplitude and the pulse [...] Read more.
Photoplethysmography (PPG) is a simple-to-perform vascular optics measurement technique that can detect blood volume changes in the microvascular bed of tissue. Beat-to-beat analysis of the PPG waveform enables the study of the variability of pulse features, such as the amplitude and the pulse arrival time (PAT), and when quantified in the time and frequency domains, has considerable potential to shed light on perfusion changes associated with peripheral arterial disease (PAD). In this pilot study, innovative multi-site bilateral finger and toe PPG recordings from 43 healthy control subjects and 31 PAD subjects were compared (recordings each at least five minutes, collected in a warm temperature-controlled room). Beat-to-beat normalized amplitude variability and PAT variability were then quantified in the time-domain using two simple statistical measures and in the frequency-domain bilaterally using magnitude squared coherence (MSC). Significantly reduced normalized amplitude variability (healthy control 0.0384 (interquartile range 0.0217–0.0744) vs. PAD 0.0160 (0.0080–0.0338) (p < 0.0001)) and significantly increased PAT variability (healthy control 0.0063 (0.0052–0.0086) vs. PAD 0.0093 (0.0078–0.0144) (p < 0.0001)) was demonstrated for the toe site in PAD using the time-domain analysis. Frequency-domain analysis demonstrated significantly lower MSC values across a range of frequency bands for PAD patients. These changes suggest a loss of right-to-left body side coherence and cardiovascular control in PAD. This study has also demonstrated the feasibility of using these measurement and analysis methods in studies investigating multi-site PPG variability for a wide range of cardiac and vascular patient groups. Full article
(This article belongs to the Special Issue Non-invasive Diagnostics for Cardiovascular Diseases)
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12 pages, 12466 KiB  
Article
The Voice of the Heart: Vowel-Like Sound in Pulmonary Artery Hypertension
by Mohamed Elgendi, Prashant Bobhate, Shreepal Jain, Long Guo, Jennifer Rutledge, Yashu Coe, Roger Zemp, Dale Schuurmans and Ian Adatia
Diseases 2018, 6(2), 26; https://doi.org/10.3390/diseases6020026 - 13 Apr 2018
Cited by 7 | Viewed by 5368
Abstract
Increased blood pressure in the pulmonary artery is referred to as pulmonary hypertension and often is linked to loud pulmonic valve closures. For the purpose of this paper, it was hypothesized that pulmonary circulation vibrations will create sounds similar to sounds created by [...] Read more.
Increased blood pressure in the pulmonary artery is referred to as pulmonary hypertension and often is linked to loud pulmonic valve closures. For the purpose of this paper, it was hypothesized that pulmonary circulation vibrations will create sounds similar to sounds created by vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) could have unique sound signatures across four auscultatory sites. Using a digital stethoscope, heart sounds were recorded at the cardiac apex, 2nd left intercostal space (2LICS), 2nd right intercostal space (2RICS), and 4th left intercostal space (4LICS) undergoing simultaneous cardiac catheterization. From the collected heart sounds, relative power of the frequency band, energy of the sinusoid formants, and entropy were extracted. PAH subjects were differentiated by applying the linear discriminant analysis with leave-one-out cross-validation. The entropy of the first sinusoid formant decreased significantly in subjects with a mean pulmonary artery pressure (mPAp) ≥ 25 mmHg versus subjects with a mPAp < 25 mmHg with a sensitivity of 84% and specificity of 88.57%, within a 10-s optimized window length for heart sounds recorded at the 2LICS. First sinusoid formant entropy reduction of heart sounds in PAH subjects suggests the existence of a vowel-like pattern. Pattern analysis revealed a unique sound signature, which could be used in non-invasive screening tools. Full article
(This article belongs to the Special Issue Non-invasive Diagnostics for Cardiovascular Diseases)
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7 pages, 1543 KiB  
Article
Toward Generating More Diagnostic Features from Photoplethysmogram Waveforms
by Mohamed Elgendi, Yongbo Liang and Rabab Ward
Diseases 2018, 6(1), 20; https://doi.org/10.3390/diseases6010020 - 11 Mar 2018
Cited by 72 | Viewed by 7746
Abstract
Photoplethysmogram (PPG) signals collected using a pulse oximeter are increasingly being used for screening and diagnosis purposes. Because of the non-invasive, cost-effective, and easy-to-use nature of the pulse oximeter, clinicians and biomedical engineers are investigating how PPG signals can help in the management [...] Read more.
Photoplethysmogram (PPG) signals collected using a pulse oximeter are increasingly being used for screening and diagnosis purposes. Because of the non-invasive, cost-effective, and easy-to-use nature of the pulse oximeter, clinicians and biomedical engineers are investigating how PPG signals can help in the management of many medical conditions, especially for global health application. The study of PPG signal analysis is relatively new compared to research in electrocardiogram signals, for instance; however, we anticipate that in the near future blood pressure, cardiac output, and other clinical parameters will be measured from wearable devices that collect PPG signals, based on the signal’s vast potential. This article attempts to organize and standardize the names of PPG waveforms to ensure consistent terminologies, thereby helping the rapid developments in this research area, decreasing the disconnect within and among different disciplines, and increasing the number of features generated from PPG waveforms. Full article
(This article belongs to the Special Issue Non-invasive Diagnostics for Cardiovascular Diseases)
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3 pages, 164 KiB  
Short Note
Less Is More in Biosignal Analysis: Compressed Data Could Open the Door to Faster and Better Diagnosis
by Mohamed Elgendi
Diseases 2018, 6(1), 18; https://doi.org/10.3390/diseases6010018 - 24 Feb 2018
Cited by 11 | Viewed by 3262
Abstract
In the digital medicine field, biosignals, such as those of an electrocardiogram (ECG), are collected regularly for screening and diagnosis, and there continues to be an increasingly substantial shift towards collecting long-term ECG signals for remote monitoring, e.g., in smart homes. ECG signal [...] Read more.
In the digital medicine field, biosignals, such as those of an electrocardiogram (ECG), are collected regularly for screening and diagnosis, and there continues to be an increasingly substantial shift towards collecting long-term ECG signals for remote monitoring, e.g., in smart homes. ECG signal collection is quite simple and only requires the use of inexpensive sensors, an active Internet connection, and a mobile device that acts as the medium between the sensors and the Internet (e.g., a mobile phone or laptop). Despite the ease and convenience of remote ECG data collection and transmission, the amount of time and energy required for the related remote computational processes remains a major limitation. This short note discusses a biosignal approach that uses fewer biomedical data for screening and diagnosis that is, compared to current data collection methods, equally, if not more, efficient. Full article
(This article belongs to the Special Issue Non-invasive Diagnostics for Cardiovascular Diseases)
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