Electronic Stethoscope With Wireless Communication to a Smart-phone, Including a Signal Filtering and Segmentation Algorithm of Digital Phonocardiography Signals

Reynolds, Jorge; Forero, J.; Botero, Juan Diego; Leguizamón, Vivian; Ramirez, Luis D.; Carlos, Lozano

doi:10.1007/978-981-10-4086-3_139

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…With the recent advancement in electronic technology, the digital stethoscope is gaining popularity day-by-day [12]. There are different types of digital stethoscopes [13,14,15,16] that have been industrialized to replace its analog counterpart. Electronic stethoscopes can provide better sound quality with variable amplification, minimize interference noise, and provide data for visualization and storage.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring

Chowdhury

Khandakar

Alzoubi

et al. 2019

Sensors

125

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One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

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Section: Introductionmentioning

confidence: 99%

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring

Chowdhury

Khandakar

Alzoubi

et al. 2019

Sensors

125

View full text Add to dashboard Cite

show abstract

“…Therefore, it will be helpful to develop an efficient method to recognize lung sounds and to diagnose related diseases based on the analysis of the more informative lung sounds data collected by electronic stethoscope and develop an efficient way to recognize lung sounds and its related diseases 7,8. During diagnosis process, it is helpful for accurately judging characteristic vectors and determining disease reasons to de-noise lung sounds and abstract vibration frequency, sound waves amplitude and amplitude gradient 9-12.…”

Section: Introductionmentioning

confidence: 99%

A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network

Shi¹,

Li²,

Cai³

et al. 2019

Int. J. Biol. Sci.

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In this paper, a method of characteristic extraction and recognition on lung sounds is given. Wavelet de-noised method is adopted to reduce noise of collected lung sounds and extract wavelet characteristic coefficients of the de-noised lung sounds by wavelet decomposition. Considering the problem that lung sounds characteristic vectors are of high dimensions after wavelet decomposition and reconstruction, a new method is proposed to transform the characteristic vectors from reconstructed signals into reconstructed signal energy. In addition, we use linear discriminant analysis (LDA) to reduce the dimension of characteristic vectors for comparison in order to obtain a more efficient way for recognition. Finally, we use BP neural network to carry out lung sounds recognition where comparatively high-dimensional characteristic vectors and low- dimensional vectors are set as input and lung sounds categories as output with a recognition accuracy of 82.5% and 92.5%.

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Electronic Stethoscope With Wireless Communication to a Smart-phone, Including a Signal Filtering and Segmentation Algorithm of Digital Phonocardiography Signals

Cited by 2 publications

References 2 publications

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring

Real-Time Smart-Digital Stethoscope System for Heart Diseases Monitoring

A Lung Sound Category Recognition Method Based on Wavelet Decomposition and BP Neural Network

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