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.

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

Analysis of the ECG signal is the prevalent method for diagnosing cardiac arrhythmia. In order to achieve a precise diagnosis, the physician must carefully examine the quantity, location, and relations between the ECG signal elements, with emphasis given to the atrial electrical activity (AEA) wave characteristics. Nevertheless, in some cases the AEA-waves are hidden in other waves, and in order to classify the correct arrhythmia an invasive procedure is performed. We propose a fully automated computer-based method for arrhythmia classification, based on our recently developed AEA detection algorithm, combined with two extracted rhythm-based features and a clinically oriented set of rules. Twenty-nine patients presenting atrioventricular nodal reentry tachycardia, atrioventricular reentry tachycardia, sinus tachycardia, atrial flutter, and sinus rhythm were studied. The arrhythmia classifier achieved 92.2% accuracy, 83.9% sensitivity, and 94.9% specificity.

Cardiac arrhythmia classification in 12-lead ECG using synthetic atrial activity signal

Speaker recognition is used today in a wide range of applications. The presence of reverberation, in hands-free systems for example, results in performance degradation. The effect of reverberation on the feature vectors and its relation to optimal GMM order are investigated. Optimal model order is calculated in terms of minimum BIC and KIC, and tested for EER of a GMM-based speaker recognition system. Experimental results show that for high reverberation time, reducing model order reduces EER values of speaker recognition. The effect of CMS on state of the art GMM and AGMM-based speaker recognition systems is investigated for reverberant speech. Results show that high reverberation time reduces the effectiveness of CMS.

The Effect of GMM Order and CMS on Speaker Recognition with Reverberant Speech

Purpose This study aims to develop and test a new computer-aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities. Methods The proposed scheme is based on the analysis of both local and global bilateral mammographic feature asymmetries. The level of local or global asymmetry is assessed by analyzing mammographic features extracted from the bilaterally matched regions of interest (ROIs), or from the entire breast, respectively. The selected local and global feature vectors are combined and classified using a maximum likelihood obtained from a naive Bayes classifier. This scheme was evaluated using a leave-one-case-out cross-validation method that was applied to 243 subjects from mini-MIAS and INbreast databases. In addition, the result is compared with a conventional unilateral (or single) image-based CAD scheme. Results Using a case-based evaluation approach and an area under curve (AUC) of the receiver operating characteristic (ROC) as a performance index, the new scheme yielded AUC = 0.79 ± 0.07, an 8.2% increase compared with AUC = 0.73 ± 0.08 obtained using the unilateral image-based CAD scheme. Conclusions This work demonstrates that applying bilateral asymmetry analysis increases the discriminatory power of CAD schemes while optimizing the likelihood assessment of breast abnormalities presence. Therefore, the proposed CAD approach provides the radiologist with beneficial supplementary information and can indicate high-risk cases.

A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry.

The performance of speaker recognition systems may degrade significantly when speech is recorded in reverberant environments by a microphone positioned far from the speaker. Most of the literature on speaker recognition uses the reverberation time to classify the reverberation effects. However, as described in this work, the reverberation time is mainly a room feature and is less affected by the distance between the source and the microphone. This paper presents a comprehensive study of room acoustics parameters and their relationship with speaker recognition performance. The definition and centra-time, acoustic parameters which are affected by both room properties and distance, were found to be more correlated with the degradation in the speaker recognition performance.

Room Acoustics Parameters Affecting Speaker Recognition Degradation Under Reverberation

Obstructive sleep apnea (OSA) affects up to 14% of the population. OSA is characterized by recurrent apneas and hypopneas during sleep. The apnea-hypopnea index (AHI) is frequently used as a measure of OSA severity. In the current study, we explored the acoustic characteristics of hypopnea in order to distinguish it from apnea. We hypothesize that we can find audio-based features that can discriminate between apnea, hypopnea and normal breathing events. Whole night audio recordings were performed using a non-contact microphone on 44 subjects, simultaneously with the polysomnography study (PSG). Recordings were segmented into 2015 apnea, hypopnea, and normal breath events and were divided to design and validation groups. A classification system was built using a 3-class cubic-kernelled support vector machine (SVM) classifier. Its input is a 36-dimensional audio-based feature vector that was extracted from each event. Three-class accuracy rate using the hold-out method was 84.7%. A two-class model to separate apneic events (apneas and hypopneas) from normal breath exhibited accuracy rate of 94.7%. Here we show that it is possible to detect apneas or hypopneas from whole night audio signals. This might provide more insight about a patient's level of upper airway obstruction during sleep. This approach may be used for OSA severity screening and AHI estimation.

Yaniv Zigel

Papers

Cardiac arrhythmia classification in 12-lead ECG using synthetic atrial activity signal

The Effect of GMM Order and CMS on Speaker Recognition with Reverberant Speech

A new computer-aided detection approach based on analysis of local and global mammographic feature asymmetry.

Room Acoustics Parameters Affecting Speaker Recognition Degradation Under Reverberation

Can we discriminate between apnea and hypopnea using audio signals