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Objective: To improve home monitoring of heart failure patients so as to reduce emergency room visits and hospital readmissions. We aim to do this by analyzing the ballistocardiogram (BCG) to evaluate the clinical state of the patient. Methods: 1) High quality BCG signals were collected at home from HF patients after discharge. 2) The BCG recordings were preprocessed to exclude outliers and artifacts. 3) Parameters of the BCG that contain information about the cardiovascular system were extracted. These features were used for the task of classification of the BCG recording based on the status of HF. Results: The best AUC score for the task of classification obtained was 0.78 using slight variant of the leave one subject out validation method. Conclusion: This work demonstrates that high quality BCG signals can be collected in a home environment and used to detect the clinical state of HF patients. Significance: In future work, a clinician/caregiver can be introduced into the system so that appropriate interventions can be performed based on the clinical state monitored at home.

Study Objective Validate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors. Methods Mice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video. A sleep-wake scoring method based on EF sensor data was developed with reference to EEG/EMG and then validated by three expert scorers. Additionally, novice scorers without sleep-wake scoring experience were self-trained to score sleep using only the EF sensor data, and results were compared to those from expert scorers. Lastly, ability to capture three-state sleep-wake staging with EF sensors attached to traditional mouse home-cages was tested. Results EF sensors quantified wake, rapid eye movement (REM) sleep, and non-REM sleep with high agreement (>93%) and comparable inter- and intra-scorer error as EEG/EMG. Novice scorers successfully learned sleep-wake scoring using only EF sensor data and scoring criteria, and achieved high agreement with expert scorers (>91%). When applied to traditional home-cages, EF sensors enabled classification of three-state (wake, NREM and REM) sleep-wake independent of EEG/EMG. Conclusions EF sensors score three-state sleep-wake architecture with high agreement to conventional EEG/EMG sleep-wake scoring 1) without invasive surgery, 2) from outside the home-cage, and 3) and without requiring specialized training or equipment. EF sensors provide an alternative method to assess rodent sleep for animal models and research laboratories in which EEG/EMG is not possible or where noninvasive approaches are preferred.

Media recordings, when captured close to active power system components, are known to be influenced by the electromagnetic interference caused by those power grid components. This electromagnetic interference manifests itself in such media recordings in the form of time-varying frequency components around the electric network frequency (ENF) of the power grid. For example, the ENF of the Indian power grid has a nominal value 50Hz. Classification of a given media signal into the grid or region of recording using the electric network frequency (ENF) is vital in location forensics. In this work, we use power recordings and audio recordings captured from 12 different grids around the globe. To use the variations in the ENF from the media signals for region-of-recording classification, we propose a high resolution ENF extraction technique. We also propose the use of a multi-stage support vector machine (SVM) based classification system. We find that the proposed system outperforms the existing baseline scheme for region-of-recording classification, by yielding an improvement in the overall accuracy by 17.33%.

A typical solution for the speech rate estimation consists of two stages, which involves first computing a short-time feature contour such that most of peaks of the contour correspond to the syllable nuclei followed by the detection of the peaks of the contour corresponding to the syllable nuclei. Temporal correlation selected sub-band correlation (TCSSBC) is often used as a feature contour for the speech rate estimation in which correlation within and across a few selected sub-band energies are computed. In this work, instead of a fixed set of sub-bands, we learn them in a data-driven manner using a dictionary learning approach. Similarly, instead of the energy contours, we use the activation profile from the learned dictionary elements. We found that the peaks detected from the data-driven approach significantly improve the speech rate estimation when combined with the traditional TCSSBC approach using a proposed peak-merging strategy. Experiments are performed separately using Switchboard, TIMIT and CTIMIT corpora. Except Switchboard, the correlation coefficient for the speech rate estimation using the proposed approach is found to be higher than those by the TCSSBC technique - 3.1% and 5.2% (relative) improvements for TIMIT and CTIMIT respectively.