Short PCG classification based on deep learning

Singh, Sinam Ajitkumar; Meitei, Takhellambam Gautam; Majumder, Swanirbhar

doi:10.1016/b978-0-12-819061-6.00006-9

Cited by 29 publications

(22 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are some researches that apply deep learning and deep neural networks in heartbeat abnormality detection [19][20][21][22][23]. Despite the high accuracy, deep learning architectures are limited with the decision-making time, since the electronic stethoscope must make a decision promptly.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection

Omarov¹,

Saparkhojayev²,

Shekerbekova³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of an electronic stethoscope, (2) a decision-making subsystem, and (3) a subsystem for displaying and visualizing the results in an understandable form. The electronic stethoscope captures the patient's phonocardiographic sounds, filters and digitizes them, and then sends the resulting phonocardiographic sounds to the decision-making system. The decision-making system classifies sounds into normal and abnormal using machine learning techniques, and as a result identifies abnormal heart sounds. The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff, but also for patients and recommends further actions to patients. As a result of the study, we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%. More accurately, the proposed stethoscope can identify normal heart sounds with 93.5% accuracy, abnormal heart sounds with 93.25% accuracy. Moreover, speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence in Medicine: Real Time Electronic Stethoscope for Heart Diseases Detection

Omarov¹,

Saparkhojayev²,

Shekerbekova³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Moreover, the occurrence of various heart diseases and S1 and S2 changes leads to an error in detecting the heart sound’s main peak and consequently prevents the determination of cycles and systolic and diastolic locations. 14 The mentioned points make segmentation a problematic stage, the degree of reliability of which affects the final identification accuracy of the diagnostic system. On the other hand, given that the extracted features are in the frequency domain, it did not matter where the input sound data starting point was located in a cycle, and only the information about the complete cardiac cycle from every input data mattered.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…In two studies conducted by Singh 14 and Thomae, 17 in addition to the selective investigation of data, a deep learning method has been used for the diagnosis. In order to train and achieve the optimal answer, this method requires a large amount of input data and complex and extensive hardware facilities.…”

Section: Basic Concepts and Literature Reviewmentioning

confidence: 99%

“…The first 3.6 and 20 s of each record in the B and E data groups of the Physionet dataset were segregated from them in Hamidi’s 15 technique, and data having a duration shorter than these periods was eliminated from the whole analysis. The rest of the data was removed from the analysis in Singh’s 14,16 investigations by only evaluating the first 5 s of the input data. However, due to the instability of the recording sensor and its incorrect settings, the appearance of non-heart sounds such as echoes and hisses at the beginning of some of the heart sound samples has caused the investigations to be diverted from the accurate diagnosis.…”

Section: Comparisonmentioning

confidence: 99%

“…Accordingly, this step has been omitted in the present study. In a number of previous unsegmented studies, the datasets have been partially investigated, 14–17 such that only a part of the data (both in the time and frequency domains) has been analyzed. In some cases, the content of the data has been changed, 17 or investigations have been conducted on a small number of people under the same sampling conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective features extraction by analyzing heart sound for identifying cardiovascular diseases related to COVID-19: A diagnostic model

Sabouri

Ghadimi

Kiani-Sarkaleh

et al. 2022

Proc Inst Mech Eng H

View full text Add to dashboard Cite

Incidence and exacerbation of some of the cardiovascular diseases in the presence of the coronavirus will lead to an increase in the mortality rate among patients. Therefore, early diagnosis of such diseases is critical, especially during the COVID-19 pandemic (mild COVID-19 infection). Thus, for diagnosing the heart diseases related to the COVID-19, an automatic, non-invasive, and inexpensive method based on the heart sound processing approach is proposed. In the present study, a set of features related to the nature of heart signals is defined and extracted. The investigated features included morphological and statistical features in the heart sound frequencies. By extracting and selecting a set of effective features related to the mentioned diseases, and avoiding to use different segmentation and filtering techniques, dependence on a limited dataset and specific sampling procedures has been eliminated. Different classifiers with various kernels are applied for diagnosis in data unbalanced and balanced conditions. The results showed 93.15% accuracy and 93.72% F1-score using 60 effective features in data balanced conditions. The identification system using the extracted features from Azad dataset is able to achieve the desired results in a generalized dataset. In this way, in the shortest possible sampling time, the present system provided an effective and generalizable method and a practical model for diagnosing important cardiovascular diseases in the presence of coronavirus in the COVID-19 pandemic.

show abstract