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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

The fetal ECG can serve as a tool for fetal distress detection. However, the abdominal ECG of a pregnant woman contains mainly the maternal ECG and a relatively small amplitude fetal ECG signal, contaminated by various noises. As part of the 2013 PhysioNet/CinC Challenge, this study aimed to develop an algorithm for noninvasive fetal QRS detection. The proposed algorithm is mainly based on fetal ECG source signal enhancement using a modified linear combiner. After initial noise reduction, the maternal QRS complexes are detected. Then, fetal QRS candidates are found. For each candidate, a Gaussian-like synthetic fetal QRS signal is created. This signal is considered an observation signal for a modified linear combiner. The 4 filtered abdomen ECG signals then undergo maternal ECG cancellation and serve as reference signals in this linear combiner; hence by finding the appropriate weight coefficients, their linear combination is forced to converge to a signal that represents the fetal QRS complexes solely. The method was developed using the entire 75 1minute-long abdomen ECG training set, and its evaluation on the 100 abdomen ECG test set led to scores of 262.076 for event 4 (fetal heart rate measurement) and 27.848 for event 5 (fetal RR interval measurement).

http://www.cinc.org/archives/2013/pdf/0169.pdf

Noninvasive fetal QRS detection using a linear combination of abdomen ECG signals

An embodiment of the invention provides a method of diagnosing obstructive sleep apnea, the method comprising: acquiring a sleep sound signal comprising sounds made by a person during sleep; detecting a plurality of snore sounds in the sleep sound signal; determining a set of mel-frequency cepstral coefficients for each of the snore sounds; determining a characterizing feature for the sleep sound signal responsive to a sum of the variances of the cepstral coefficients; and using the characterizing feature to diagnose obstructive sleep apnea in the person.

Apparatus and method for diagnosing obstructive sleep apnea

Obstructive sleep apnea (OSA) is a sleep disorder associated with several anatomical abnormalities of the upper airway. Our hypothesis is that it is possible to distinguish between OSA and non-OSA subjects by analyzing particular speech signal properties using an automatic computerized system. The database for this research was constructed from 90 male subjects who were recorded reading a one-minute speech protocol immediately prior to a full polysomnography study; specific phonemes were isolated using closed group phoneme identification; seven independent Gaussian mixture models (GMM)-based classifiers were implemented for the task of OSA \ non-OSA classification; a fusion process was designed to combine the scores of these classifiers and a validation procedure took place in order to examine the system’s performance. Results of 91.66% specificity and 91.66% sensitivity were achieved using a leave one out procedure when the data was manually segmented. The system performances were somewhat decreased when the automatic segmentation was used, resulting in 83.33% specificity and 81.25% sensitivity.

Detection of obstructive sleep apnea using speech signal analysis.

Setting: We developed an algorithm to assess recorded cough episodes and differentiate them from similar, non-cough sounds. Objective: To measure cough episodes in healthy young adults, cigarette smokers and non-smokers over a 24-hour recording period, during the course of normal activity. Design: The study subjects were students, aged 20–40 years old. 24-hour sound recordings were conducted by a portable recorder. Analysis used an algorithm that was developed and tested in the study. Results: Seventy students were recruited. Recordings included 2628 cough episodes in 1704 h of recording. The cough detection algorithm successfully detected 88.5% of recorded cough episodes and 95.6% of non-cough sounds. There was a clear tendency for more coughs among smokers. Autumn was the season with the highest mean cough episodes per day (58.65), while summer had the lowest (14.19). There was a strong correlation between self-reported cough episodes and recorded coughs. Cough episodes were significantly more f...

Diurnal and seasonal variation of cough episodes in healthy young adults.

A new method called separation using maximum energy ratio (SUMER) is introduced. Using 12 lead ECG signals, SUMER tries to separate the atrial activity from the ventricular activity. Relying on the assumption that the atrial activity can be reconstructed from a linear combination of 12 lead ECG signals, SUMER looks for the combination that will give us the best representation of the atrial activity. A cost function that is the energy ratio between different segments in the ECG signal is created. Forcing the linear combination to find the maximum possible cost function gives us the desired combination. The method presents better separation performances than the ICA method, and provides tools for the next phase of P wave detection.

/pdf/a-new-method-for-atrial-electrical-activity-analysis-from-3hqfs4t6zx.pdf

Yaniv Zigel

Papers

Noninvasive fetal QRS detection using a linear combination of abdomen ECG signals

Apparatus and method for diagnosing obstructive sleep apnea

Detection of obstructive sleep apnea using speech signal analysis.

Diurnal and seasonal variation of cough episodes in healthy young adults.

A new method for atrial electrical activity analysis from surface ECG signals using an energy ratio measure