Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals

Zhong, Meisu; Yang, Yongsheng; Zhou, Yuqing; Postolache, Octavian; Chandrasekar, M.; Babu, G. Venkat; Manikandan, C.; Balaji, V. S.; Saravanan, S.; Elamaran, V.

doi:10.1166/jmihi.2020.3127

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compression reduces the amount of bandwidth necessary for wireless communication, while low-power wireless technology helps to save money. It also reduces the amount of space required to store ECG data [ 35 – 37 ].…”

Section: Methodsmentioning

confidence: 99%

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Jerard

Thilagaraj

Pandiaraj

et al. 2021

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

Recent advances in electronics and microelectronics have aided the development of low-cost devices that are widely used as well-being or preventive monitoring devices by many people. Remote health monitoring, which includes wearable sensors, actuators, and modern communication and information systems, offers effective programs that allow people to live peacefully in their own homes while also being protected in some way. High-frequency noise, power-line interface, and baseline drift are prevalent during the data-acquisition system of an ECG signal, and they can limit signal understanding. They (noises) must be isolated in order to provide an appropriate diagnostic of the patient. When removing high-frequency components (noise) from an ECG signal with an FIR filter, the critical path delay increases considerably as the filter's duration increases. To reduce high-frequency noise, simple moving average filters with pipelining and look-ahead transformation techniques are extensively used in this study. With the use of pipelining and look-ahead techniques, the only objective is to increase the clock speed of the designs. The moving average filters (conventional and proposed) were created on an Altera Cyclone IV FPGA EP4CE115F29C7 chip using the Quartus II software v13.1 tool. Finally, performance metrics such logic elements, clock speed, and power consumption were compared and studied thoroughly. The recursive pipelined 8-tap MA filter with look-ahead approach outperforms the other designs (685.48 MHz) in this investigation.

show abstract

Section: Methodsmentioning

confidence: 99%

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Jerard

Thilagaraj

Pandiaraj

et al. 2021

Journal of Healthcare Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are several methods needed in medical imaging that depend on digital signal processing (DSP), such as convolution, discrete Fourier transform (DFT), fast Fourier transform (FFT), finite impulse response (FIR) and infinite impulse response (IIR) filters, FFT recursive and non-recursive digital filters, FFT processing, random signal theory, adaptive filters, upsampling and downsampling, etc. [94].…”

Section: Dsps In Medical Imagingmentioning

confidence: 99%

“…In general, low pass, high pass, band pass and band reject filters are implemented for filtering functions. It is common to employ these filters using DSPs for biomedical engineering fields such as MRI, ultrasound, CT, X-ray or PET imaging, as well as the analysis and processing of signals derived from these or genetic signals [94].…”

Section: Dsps In Medical Imagingmentioning

confidence: 99%

Hardware Architectures for Real-Time Medical Imaging

et al. 2021

View full text Add to dashboard Cite

Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.

show abstract

“…The detection and analyses of heart sounds are an important and economical means to understand the physiological information of the human body and have proven to be valuable for disease detection and biometric identification [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. The most common method for the extraction of heart sound features is time–frequency analysis, represented by the wavelet transform method [ 4 , 10 , 11 , 12 ], and power spectrum analysis, represented by the FFT method [ 13 , 14 , 15 , 16 , 17 ]. Moreover, according to the premise of these analyses, heart-sound signals must be collected in a resting state.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Heart-Sound Characteristics during Motion Based on a Graphic Representation

She

Cheng

Wang

2021

Sensors

View full text Add to dashboard Cite

In this paper, the graphic representation method is used to study the multiple characteristics of heart sounds from a resting state to a state of motion based on single- and four-channel heart-sound signals. Based on the concept of integration, we explore the representation method of heart sound and blood pressure during motion. To develop a single- and four-channel heart-sound collector, we propose new concepts such as a sound-direction vector of heart sound, a motion–response curve of heart sound, the difference value, and a state-change-trend diagram. Based on the acoustic principle, the reasons for the differences between multiple-channel heart-sound signals are analyzed. Through a comparative analysis of four-channel motion and resting-heart sounds, from a resting state to a state of motion, the maximum and minimum similarity distances in the corresponding state-change-trend graphs were found to be 0.0038 and 0.0006, respectively. In addition, we provide several characteristic parameters that are both sensitive (such as heart sound amplitude, blood pressure, systolic duration, and diastolic duration) and insensitive (such as sound-direction vector, state-change-trend diagram, and difference value) to motion, thus providing a new technique for the diverse analysis of heart sounds in motion.

show abstract

Advanced Digital Signal Processing Techniques on the Classification of the Heart Sound Signals

Cited by 5 publications

References 0 publications

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Reconfigurable Architectures with High-Frequency Noise Suppression for Wearable ECG Devices

Hardware Architectures for Real-Time Medical Imaging

Analysis of Heart-Sound Characteristics during Motion Based on a Graphic Representation

Contact Info

Product

Resources

About