ECG signal denoising using higher order statistics in Wavelet subbands

A power control system/method implementing Internet based access to hybrid home automation networks is disclosed. The system utilizes a smart gateway power controller (SGPC) to selectively switch an AC power source to a load device under control of local or remote network commands that may be routed through a variety of network interfaces and protocols present within a home or other structure- local communications network. SGPC configurations may be nested within a home automation network to permit separation of control for load devices within a common home automation environment. Present invention methods may include routing protocols between disparate home automation networks as well as remote access protocols that permit control of disparate home automation networks via the Internet using a wide variety of remote access interfaces including mobile devices, tablet computers, laptops, desktop computers, and the like.

Power control system and method

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.

Multichannel ECG Data Compression Based on Multiscale Principal Component Analysis

The proliferation of embedded systems, wireless technologies, and Internet protocols have enabled the Internet of Things (IoT) to bridge the gap between the virtual and physical world through enabling the monitoring and actuation of the physical world controlled by data processing systems. Wireless technologies, despite their offered convenience, flexibility, low cost, and mobility pose unique challenges such as fading, interference, energy, and security, which must be carefully addressed when using resource-constrained IoT devices. To this end, the efforts of the research community have led to the standardization of several wireless technologies for various types of application domains depending on factors such as reliability, latency, scalability, and energy efficiency. In this paper, we first overview these standard wireless technologies, and we specifically study the MAC and physical layer technologies proposed to address the requirements and challenges of wireless communications. Furthermore, we explain the use of these standards in various application domains, such as smart homes, smart healthcare, industrial automation, and smart cities, and discuss their suitability in satisfying the requirements of these applications. In addition to proposing guidelines to weigh the pros and cons of each standard for an application at hand, we also examine what new strategies can be exploited to overcome existing challenges and support emerging IoT applications.

https://hal.archives-ouvertes.fr/hal-02161803/document

Low-Power Wireless for the Internet of Things: Standards and Applications

An apparatus and method for encoding and decoding a signal for high frequency bandwidth extension are provided. An encoding apparatus may down-sample a time domain input signal, may core-encode the down-sampled time domain input signal, may transform the core-encoded time domain input signal to a frequency domain input signal, and may perform bandwidth extension encoding using a basic signal of the frequency domain input signal.

Apparatus and method for encoding and decoding signal for high frequency bandwidth extension

The use of a Zigbee network in various health monitoring systems has become well accepted. This network promises to develop health care by allowing prompt, secure, low power, and low-cost health monitoring with real-time medical data transmission and update via the session initiation protocol (SIP). In this paper, we present a description and system architecture for a ubiquitous ECG monitoring system based on SIP and the Zigbee Network. Our proposed ubiquitous ECG monitoring system includes a wireless ECG sensor, ECG console, a Zigbee module, a SIP registrar and proxy server, a database server, and wireless devices for users. This system specifically targets patients, senior citizens, and others who may benefit from continuous, Remote health monitoring systems. We performed an evaluation of the results of this research through an ECG monitoring system design and experimentation with a pervasive computing test bed.

Design and Implementation of a Ubiquitous ECG Monitoring System Using SIP and the Zigbee Network

The present invention relates to a method for processing an audio signal, comprising: a step of performing a frequency conversion process on an audio signal to obtain a plurality of frequency transform coefficients; a step of selecting either a general mode or a non-general mode, on the basis of a pulse ratio, for the frequency transform coefficients having a high frequency band from among the plurality of frequency transform coefficients; and a step of performing, if the non-general mode is selected, the following steps: extracting a predetermined number of pulses from the frequency transform coefficients having the high frequency band, and generating pulse information; generating an original noise signal from the frequency transform coefficients having the high frequency band, excluding the pulses; generating a reference noise signal using the frequency transform coefficient having a low frequency band from among the plurality of frequency transform coefficients; and generating noise position information and noise energy information using the original noise signal and the reference noise signal

Method and apparatus for processing an audio signal

In this paper, an improved wavelet compression algorithm is proposed for electrocardiogram (ECG) signals which is combined with the lifting wavelet transform (WT) and the dynamic multi-stage vector quantization (MS-VQ). The lifting wavelet transformed coefficients in a hierarchical tree order are taken as the components of a vector called a tree vector (TV). Based on the property of wavelet coefficients that gives emphasis to approximation coefficients, the components of a target vector for two stages VQ is differently extracted from different WT sub- bands. In the first-stage, 32 dimensional TV for crude quantization is extracted in the order of a hierarchical tree from a WT sub-bands except the last sub-band and in the second stage, 64 dimensional code vectors from all WT sub-bands. Each codebook is adaptively updated by the distortion constrained codebook replenishment mechanism (DCCR). The combination of lifting WT and dynamic multi-stage VQ retains feature integrity of the ECG at high compression ratios. Preliminary results indicate that the proposed method excels over previous techniques for high fidelity compression.

http://ieeexplore.ieee.org/iel5/5089335/5138149/05138572.pdf

Wavelet-based ECG Compression using Dynamic

The use of a Zigbee network in various health monitoring systems has become well accepted. However, such a system has limited storage, power supply, bandwidth for communication, and processing speed when massive volumes of bio signals, especially ECG signals are involved. Various effective methods, including using low power hardware, have been proposed to maximize the limited resources. Data compression is one of the most efficient methods. In this paper, we propose an ECG compression algorithm based on a Discrete Wavelet lifting Transform (DWLT) and Multistage Vector Quantization (MSVQ) methods for a ubiquitous ECG monitoring system over the Zigbee Network. We performed an evaluation of the results of the proposed compression method through an ECG monitoring system and experimentation with a pervasive computing test bed.

DWLT compression method based on MSVQ for a real-time ECG monitoring system in WSNs

In this paper, an improved wavelet compression algorithm is proposed for electrocardiogram (ECG) signals which is combined with the lifting wavelet transform (WT) and the dynamic multi-stage vector quantization (MS-VQ). The lifting wavelet transformed coefficients in a hierarchical tree order are taken as the components of a vector called a tree vector (TV). Based on the property of wavelet coefficients that gives emphasis to approximation coefficients, the components of a target vector for two stages VQ is differently extracted from different WT subbands. In the first-stage, 32 dimensional TV for crude quantization is extracted in the order of a hierarchical tree from a WT sub-bands except the last sub-band and in the second stage, 64 dimensional code vectors from all WT sub-bands. Each codebook is adaptively updated by the distortion constrained codebook replenishment mechanism (DCCR). The combination of lifting WT and dynamic multi-stage VQ retains feature integrity of the ECG at high compression ratios. Preliminary results indicate that the proposed method excels over previous techniques for high fidelity compression.

In-Sung Lee

Papers

Design and Implementation of a Ubiquitous ECG Monitoring System Using SIP and the Zigbee Network

Method and apparatus for processing an audio signal

Wavelet-based ECG Compression using Dynamic

DWLT compression method based on MSVQ for a real-time ECG monitoring system in WSNs

Wavelet-based ECG compression using dynamic multi-stage vector quantization