Smart phone monitoring of second heart sound split

Abstract

Heart Auscultation (listening to heart sounds) is the basic element of cardiac diagnosis. The interpretation of these sounds is a difficult skill to acquire. In this work we have developed an application to detect, monitor, and analyze the split in second heart sound (S2) using a smart phone. The application records the heartbeat using a stethoscope connected to the smart phone. The audio signal is converted into the frequency domain using Fast Fourier Transform to detect the first and second heart sounds (S1 and S2). S2 is extracted and fed into the Discrete Wavelet Transform (DWT) and then to Continuous Wavelet Transform (CWT) to detect the Aortic (A2) and the Pulmonic (P2) components, which are used to calculate the split in S2. With our application, users can continuously monitor their second heart sound irrespective of ages and check for a split in their hearts with a low-cost, easily available equipment.

A Novel Method for Measuring the Timing of Heart Sound Components through Digital Phonocardiography

“…Also in the case of the components of the second heart sound, the experimental values we obtained are compliant with the physiological ranges reported in the literature and to the results obtained in former studies [1,23,24,25]. In particular, the second heart sound is expected to occur approximately 300 ms after the first one [1], which is coherent with the values reported in Table 4.…”

“…In terms of heart sound components, few detection algorithms [23,24,25,30] are reported in literature, especially concerning the first heart sound. When considering the approach described in this work, it must be observed that when a heart sound is correctly segmented both its components are identified.…”

“…Even though heart sound segmentation is a popular research subject, few is reported in the literature concerning the analysis of heart sound components. Moreover, most studies on the split between them use time frequency approaches, in some cases involving nonstationary decomposition, but they are mostly qualitative [23,24,25]. Sensitivity values of 96% are reported by Barma et al [23] for the detection of the delay among second heart sound components.…”

Edge computing in smart health care systems: Review, challenges, and research directions

“…Sensor data from an edge computing application is commonly sent longer distances to a server. Smartphones are capable of harnessing built-in sensors, such as the microphone or gyroscope, for medical purposes 39,40 . Unlike wearable and smartphone-based sensors, which are physically closer to the patient, ambient sensors are placed around a room or number of rooms to collect data on user position without the patient wearing them.…”

Improving the Sensitivity and Functionality of Mobile Webcam-Based Fluorescence Detectors for Point-of-Care Diagnostics in Global Health

“…In recent years, many mobile POCT devices have been introduced including POCT devices based on low-cost consumer electronics technologies such as smartphones or tablets, which have sophisticated computing capabilities and are widely available in LMICs [ 5 ]. These devices have been integrated into several types of transducers to provide new capabilities such as smartphone attachments for stethoscopes [ 6 , 7 , 8 ], smartphone-based ambulatory blood pressure monitoring [ 8 , 9 , 10 , 11 , 12 ], mobile point-of-care ultrasound [ 13 ], or digital microscopy [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. New assay technologies such as lateral flow, paper-based microfluidics [ 23 ], phone-based colorimetric readers [ 24 ], or lab-on-a-chip (LOC) were used for the development of sensitive, low-cost biological assays [ 25 ].…”