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

Sleep staging is essential for evaluating sleep and its disorders Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed Here, we present a pioneering approach for rapid eye movement (REM), non-REM, and wake staging (macro-sleep stages, MSS) estimation based on sleep sounds analysis Our working hypothesis is that the properties of sleep sounds, such as breathing and movement, within each MSS are different We recorded audio signals, using non-contact microphones, of 250 patients referred to a polysomnography (PSG) study in a sleep laboratory We trained an ensemble of one-layer, feedforward neural network classifiers fed by time-series of sleep sounds to produce real-time and offline analyses The audio-based system was validated and produced an epoch-by-epoch (standard 30-sec segments) agreement with PSG of 87% with Cohen's kappa of 07 This study shows the potential of audio signal analysis as a simple, convenient, and reliable MSS estimation without contact sensors

/pdf/sleep-staging-using-nocturnal-sound-analysis-cgzmd8di5f.pdf

Sleep staging using nocturnal sound analysis.

Apparatus for detection of human falls, comprises: an acceleration detector, for detecting vibration events, typically placed on a floor, a microphone, located in association with the acceleration detector for detection of corresponding sound events, and a classification unit to classify concurrent events from the microphone and the acceleration detector, thereby to determine whether a human fall is indicated. If the event appears to be a human fall, then an alarm is raised.

System for automatic fall detection for elderly people

Study Objectives
To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA).

/pdf/sleep-wake-evaluation-from-whole-night-non-contact-audio-c68kpklra2.pdf

Sleep-Wake Evaluation from Whole-Night Non-Contact Audio Recordings of Breathing Sounds

The need for efficient coding of ECG signals is continually increasing with modern use of long-term monitoring and telemedicine. Here, the authors present an algorithm based on long-term prediction (LTP) compression. The single-channel LTP algorithm has been generalized to the multichannel case and is called the MC-LTP algorithm. This algorithm compresses PQRST beats using a pattern codebook with "typical" beats.

Compression of multichannel ECG through multichannel long-term prediction

A novel method for screening obstructive sleep apnea syndrome (OSAs) based on nocturnal acoustic signal is proposed. Full-night audio signals from sixty subjects were segmented into snore, noise and silence events using semi-automatic algorithm based on Gaussian mixture models which achieves more than 90% (92%) sensitivity (specificity) and produces an average of 2,000 snores per subject. A classification into 3 groups is proposed for the diagnosis: comparison group - non-OSA subjects (apnea hypopnea index, AHI 30). A Bayes classifier was implemented, fed with five acoustic features, all correlated with the severity of the syndrome: (1) Inter Event Silence, which quantifies segments suspicious as apnea; (2) Mel Cepstability, measures the entire night stability of the spectrum, expressed using mel-frequency cepstrum; (3) Energy Running Variance, a criterion for the variation of the nocturnal acoustic pattern; (4) Apneic Phase Ratio, exploiting the finding that snores around apnea events expressing larger acoustic variation; and (5) Pitch Density. Correct classification of 92% for resubstitution method and 80% for 5-fold cross validation method was achieved. Moreover, in a case of two groups with a threshold of AHI=10, a sensitivity (specificity) of 96.5% (90.6%) and 87.5% (82.1%) for resubstitution and cross-validation respectively were obtained.

Yaniv Zigel

Papers

Sleep staging using nocturnal sound analysis.

System for automatic fall detection for elderly people

Sleep-Wake Evaluation from Whole-Night Non-Contact Audio Recordings of Breathing Sounds

Compression of multichannel ECG through multichannel long-term prediction

Nocturnal sound analysis for the diagnosis of obstructive sleep apnea