Knowledge on heart patients through stethoscopic cardiac murmur identification for E-healthcare

Udawatta, Lanka; Abeykoon, A. M. Harsha S.; Prasanga, D Kasun; Prasad, Sklb; Perera, Warp; Perera, K. N. M.

doi:10.1109/ictke.2010.5692915

Cited by 4 publications

(12 citation statements)

References 9 publications

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“…Table 5 presents the list of selected articles, containing a numerical identification, reference, authors' countries, databases, year of publication, and a short description of the research work. India IEEE Xplore Noise detection on sign Ayari et al [54] Tunisia/USA IEEE Xplore Mathematical component analysis algorithm for separation of cardiac sounds from pulmonary sounds Udawatta et al [55] Sri Lanka IEEE Xplore Digital stethoscope to amplify signal Malek et al [56] Malaysia IEEE Xplore Digital stethoscope in Arduino, ZigBee and signal processing by MatLab Singh and Singh [36] India IEEE Xplore Convolutional Neural Networks Das et al [57] India IEEE Xplore Algorithm to remove signal noise, regardless of sensor quality Gjoreski et al [58] Slovenia/Macedônia IEEE Xplore Machine Learning Pereira et al [59] Portugal/Brasil IEEE Xplore Machine Learning Banerjee et al [60] India IEEE Xplore Convolutional Neural Networks Suhn et al [61] Germany IEEE Xplore Carotid auscultation equipment Gautam and kumar [62] India IEEE Xplore Multilayer Multilayer Perceptron Artificial Neural Network Zhang et al [63] Singapore IEEE Xplore Heart rate estimation algorithm Doshi et al [64] India IEEE Xplore Neural Network Prasad et al [65] Switzerland IEEE Xplore Processing in the time domain employing a low-pass filter Rao et al [66] Switzerland IEEE Xplore Neural Network Hui et al [67] USA IEEE Xplore Investigates transient movement and heartbeat Humayun et al [25] Bangladesh/USA IEEE Xplore Use of convolutional neural network to detect abnormality of cardiac sound with stethoscope Shuvo et al [68] Bangladesh/Saudi Arabia/Yemen IEEE Xplore Convolutional Neural Network for automatic detection of different classes of cardiovascular diseases, direct by phonocardiography signal Tiwari et al [27] India/Saudi Arabia IEEE Xplore Hybrid model, with signal processing using the constant Q transform and Convolutional Neural Network Du et al [69] China JMIR Big Data and Machine Learning Chowdhury et al [26] Qatar/Malaysia PubMed Central Processing and classification using MATLAB Leng et al [30] Singapore PubMed Central Machine Learning Techniques Elgendi et al [70] Canada/India PubMed Central Developed a Wavelet-based algorithm SwarupandMakaryus [71] USA PubMed Central Use of digital stethoscope and mobile computing Raza et al…”

Section: Criteria and Filtering Resultsmentioning

confidence: 99%

“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9,11,16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

Section: Gq1-what Iot Features Are Being Used To Capture Human Chest Sound Signals?mentioning

confidence: 99%

“…In this way, IoT can improve the effectiveness of treatments, prevent risk situations and assist in health promotion. Twelve studies approached the context of remote monitoring (IDs = 5, 9, 10, 17,27,36,38,40,43,44,51,55). In addition, IoT increases resource management efficiency through flexibility and mobility using intelligent solutions.…”

Section: Gq2-what Are the Benefits For The Patient By Using Iot For Cardiac Care?mentioning

confidence: 99%

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Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

et al. 2021

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This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

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Section: Criteria and Filtering Resultsmentioning

confidence: 99%

Section: Gq1-what Iot Features Are Being Used To Capture Human Chest Sound Signals?mentioning

confidence: 99%

Section: Gq2-what Are the Benefits For The Patient By Using Iot For Cardiac Care?mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

et al. 2021

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“…The model used the Arduino, connected by Bluetooth, to the notebook for processing and analysis of the signal by the MatLab software and Android smartphone. MatLab software was used in 24 works (IDs = 2, 3, 7, 8, 9, 11, 16,17,18,24,26,27,30,35,37,39,42,46,48,54,55,56,57,58).…”

Section: Resultsmentioning

confidence: 99%

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Brites¹,

Silva²,

Barbosa³

et al. 2021

Preprint

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This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

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“…Recording of data (vibration occur when stethoscope is placed on human body) by using stethoscope and stored in memory, used for further analysis [13][14] [16]. Recorded signal is send to authorization center by using electronic mail, where expert doctor use various software to plot these signal for analysis.…”

Section: Analysis Of Signal After Recordingmentioning

confidence: 99%

Design of Electronic Stethoscope and Heart Rate Monitor for Remote Area Application

Prasad¹,

Phade²

2016

IJCA

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Developing countries like India, where doctor to patient ratio is around 1:1700, as large number of population lives in villages and remotely located area. Health checkup of such a large population is very challenging for their government due to less number of qualified doctors, highly qualified doctor refuges to work in villages and remotely placed areas. This paper proposes a new idea by which an electronic stethoscope is used to provide health checkup of such large population, by creating a public health center (PHC) in every village or combining more small villages to one PHC and connecting it with authorized center, authorized center monitors more than fifteen PHC. Recording of human organ signal by using stethoscope and transmitted to the authorized center using electronic mail can reduce the requirement of large number of doctors.

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Knowledge on heart patients through stethoscopic cardiac murmur identification for E-healthcare

Cited by 4 publications

References 9 publications

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Design of Electronic Stethoscope and Heart Rate Monitor for Remote Area Application

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