다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Digital processing of speech signals

Digital Coding of Waveforms

A survey on fall detection: Principles and approaches

Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.

/pdf/compressed-sensing-for-real-time-energy-efficient-ecg-5bqmb6npzx.pdf

Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

In this paper, a new distortion measure for electrocardiogram (ECG) signal compression, called weighted diagnostic distortion (WDD) is introduced. The WDD measure is designed for comparing the distortion between original ECG signal and reconstructed ECG signal (after compression). The WDD is based on PQRST complex diagnostic features (such as P wave duration, QT interval, T shape, ST elevation) of the original ECG signal and the reconstructed one. Unlike other conventional distortion measures [e.g. percentage root mean square (rms) difference, or PRD], the WDD contains direct diagnostic information and thus is more meaningful and useful. Four compression algorithms were implemented (AZTEC, SAPA2, LTP, ASEC) in order to evaluate the WDD. A mean opinion score (MOS) test was applied to test the quality of the reconstructed signals and to compare the quality measure (MOS/sub error/) with the proposed WDD measure and the popular PRD measure. The evaluators in the WIGS test were three independent expert cardiologists, who studied the reconstructed ECG signals in a blind and a semiblind tests. The correlation between the proposed WDD measure and the MOS test measure (MOS/sub error/) was found superior to the correlation between the popular PRD measure and the MOS/sub error/.

The weighted diagnostic distortion (WDD) measure for ECG signal compression

Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: ldquoRescue Randy.rdquo The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.

A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls

An electrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean compression rate of approximately 100 bits/s (compression ratio of about 30:1) has been achieved with a good reconstructed signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As In the quantitative test, the proposed compression algorithm was found to be superior to the other tested compression algorithms.

/pdf/ecg-signal-compression-using-analysis-by-synthesis-coding-5f7iv1zpqi.pdf

ECG signal compression using analysis by synthesis coding

A method and apparatus for determining whether a speaker uttering an utterance belongs to a predetermined set comprising known speakers, wherein a training utterance is available for each known speaker. The method and apparatus test whether features extracted from the tested utterance provide a score exceeding a threshold when matched against one or more of models constructed upon voice samples of each known speaker. The method and system further provide optional enhancements such as determining, using, and updating model normalization parameters, a fast scoring algorithm, summed calls handling, or quality evaluation for the tested utterance.

Method and apparatus for large population speaker identification in telephone interactions

Objective
Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology.

/pdf/automatic-detection-of-whole-night-snoring-events-using-non-3zl0732c6z.pdf

Yaniv Zigel

Papers

The weighted diagnostic distortion (WDD) measure for ECG signal compression

A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls

ECG signal compression using analysis by synthesis coding

Method and apparatus for large population speaker identification in telephone interactions

Automatic detection of whole night snoring events using non-contact microphone.