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A low-cost, portable, high-throughput wireless sensor system for phonocardiography applications.

Sa-Ngasoongsong A, Kunthong J, Sarangan V, Cai X, Bukkapatnam ST - Sensors (Basel) (2012)

Bottom Line: The experimental results of sensor signal analysis using several signal characterization techniques suggest that this wireless sensor system can capture both fundamental heart sounds (S1 and S2), and is also capable of capturing abnormal heart sounds (S3 and S4) and heart murmurs without aliasing.The results of a denoising application using Wavelet Transform show that the undesirable noises of sensor signals in the surrounding environment can be reduced dramatically.The exercising experiment results also show that this proposed wireless PCG system can capture heart sounds over different heart conditions simulated by varying heart rates of six subjects over a range of 60-180 Hz through exercise testing.

View Article: PubMed Central - PubMed

Affiliation: School of Industrial Engineering & Management, Oklahoma State University, Stillwater, OK 74078, USA. akkarap@okstate.edu

ABSTRACT
This paper presents the design and testing of a wireless sensor system developed using a Microchip PICDEM developer kit to acquire and monitor human heart sounds for phonocardiography applications. This system can serve as a cost-effective option to the recent developments in wireless phonocardiography sensors that have primarily focused on Bluetooth technology. This wireless sensor system has been designed and developed in-house using off-the-shelf components and open source software for remote and mobile applications. The small form factor (3.75 cm × 5 cm × 1 cm), high throughput (6,000 Hz data streaming rate), and low cost ($13 per unit for a 1,000 unit batch) of this wireless sensor system make it particularly attractive for phonocardiography and other sensing applications. The experimental results of sensor signal analysis using several signal characterization techniques suggest that this wireless sensor system can capture both fundamental heart sounds (S1 and S2), and is also capable of capturing abnormal heart sounds (S3 and S4) and heart murmurs without aliasing. The results of a denoising application using Wavelet Transform show that the undesirable noises of sensor signals in the surrounding environment can be reduced dramatically. The exercising experiment results also show that this proposed wireless PCG system can capture heart sounds over different heart conditions simulated by varying heart rates of six subjects over a range of 60-180 Hz through exercise testing.

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(a) Frequency Content of Wireless Sensor Signal using FFT; (b) Segmentation of Wireless Sensor Signal using STFT.
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f9-sensors-12-10851: (a) Frequency Content of Wireless Sensor Signal using FFT; (b) Segmentation of Wireless Sensor Signal using STFT.

Mentions: Generally, the characteristics of heart sounds include S1 and S2 locations, the number of components for each sound, their frequency content, and their time interval. As shown in Figure 8, the location, time interval, and number of components can be validated easily using the local peaks (extremas) of the sensor signal. In order to measure the quality of the wireless PCG signal, several signal characterization techniques were used to test the frequency characteristics of the sensor signals, including the Fast Fourier Transform (FFT) [36] and the Short-time Fourier Transform (STFT) [37]. The FFT is used to convert the original sensor signal from the time domain to the frequency domain. It is an efficient algorithm to compute the Discrete-Time Fourier Transform (DTFT) and its inverse. The mathematical definition of the DTFT of time series x(t), where t = 1,…,N, is given in Equation (1):(1)X(ω)=∑t=1Nx(t)e−iωtwhere t is time and ω is frequency component, respectively. The DTFT of x(t) provides an approximation of the Continuous-Time Fourier Transform of time domain signal x(t). One disadvantage of the Fourier Transform is that it only determines the frequency components of a signal. However, it can’t tell us about their location in time. So, it is not adequate to the heart sound which presents non-stationary characteristics. Figure 9(a) shows the frequency contents of the wireless sensor signal using the FFT.


A low-cost, portable, high-throughput wireless sensor system for phonocardiography applications.

Sa-Ngasoongsong A, Kunthong J, Sarangan V, Cai X, Bukkapatnam ST - Sensors (Basel) (2012)

(a) Frequency Content of Wireless Sensor Signal using FFT; (b) Segmentation of Wireless Sensor Signal using STFT.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472861&req=5

f9-sensors-12-10851: (a) Frequency Content of Wireless Sensor Signal using FFT; (b) Segmentation of Wireless Sensor Signal using STFT.
Mentions: Generally, the characteristics of heart sounds include S1 and S2 locations, the number of components for each sound, their frequency content, and their time interval. As shown in Figure 8, the location, time interval, and number of components can be validated easily using the local peaks (extremas) of the sensor signal. In order to measure the quality of the wireless PCG signal, several signal characterization techniques were used to test the frequency characteristics of the sensor signals, including the Fast Fourier Transform (FFT) [36] and the Short-time Fourier Transform (STFT) [37]. The FFT is used to convert the original sensor signal from the time domain to the frequency domain. It is an efficient algorithm to compute the Discrete-Time Fourier Transform (DTFT) and its inverse. The mathematical definition of the DTFT of time series x(t), where t = 1,…,N, is given in Equation (1):(1)X(ω)=∑t=1Nx(t)e−iωtwhere t is time and ω is frequency component, respectively. The DTFT of x(t) provides an approximation of the Continuous-Time Fourier Transform of time domain signal x(t). One disadvantage of the Fourier Transform is that it only determines the frequency components of a signal. However, it can’t tell us about their location in time. So, it is not adequate to the heart sound which presents non-stationary characteristics. Figure 9(a) shows the frequency contents of the wireless sensor signal using the FFT.

Bottom Line: The experimental results of sensor signal analysis using several signal characterization techniques suggest that this wireless sensor system can capture both fundamental heart sounds (S1 and S2), and is also capable of capturing abnormal heart sounds (S3 and S4) and heart murmurs without aliasing.The results of a denoising application using Wavelet Transform show that the undesirable noises of sensor signals in the surrounding environment can be reduced dramatically.The exercising experiment results also show that this proposed wireless PCG system can capture heart sounds over different heart conditions simulated by varying heart rates of six subjects over a range of 60-180 Hz through exercise testing.

View Article: PubMed Central - PubMed

Affiliation: School of Industrial Engineering & Management, Oklahoma State University, Stillwater, OK 74078, USA. akkarap@okstate.edu

ABSTRACT
This paper presents the design and testing of a wireless sensor system developed using a Microchip PICDEM developer kit to acquire and monitor human heart sounds for phonocardiography applications. This system can serve as a cost-effective option to the recent developments in wireless phonocardiography sensors that have primarily focused on Bluetooth technology. This wireless sensor system has been designed and developed in-house using off-the-shelf components and open source software for remote and mobile applications. The small form factor (3.75 cm × 5 cm × 1 cm), high throughput (6,000 Hz data streaming rate), and low cost ($13 per unit for a 1,000 unit batch) of this wireless sensor system make it particularly attractive for phonocardiography and other sensing applications. The experimental results of sensor signal analysis using several signal characterization techniques suggest that this wireless sensor system can capture both fundamental heart sounds (S1 and S2), and is also capable of capturing abnormal heart sounds (S3 and S4) and heart murmurs without aliasing. The results of a denoising application using Wavelet Transform show that the undesirable noises of sensor signals in the surrounding environment can be reduced dramatically. The exercising experiment results also show that this proposed wireless PCG system can capture heart sounds over different heart conditions simulated by varying heart rates of six subjects over a range of 60-180 Hz through exercise testing.

Show MeSH
Related in: MedlinePlus