Nandakumar Selvaraj

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Unobtrusive continuous monitoring of important vital signs and activity metrics has the potential to provide remote health monitoring, at-home screening, and rapid notification of critical events such as heart attacks, falls, or respiratory distress. This paper contains validation results of a wireless Bluetooth Low Energy (BLE) patch sensor consisting of(More)
Accurate and early detection of blood volume loss would greatly improve intraoperative and trauma care. This study has attempted to determine early diagnostic and quantitative markers for blood volume loss by analyzing photoplethysmogram (PPG) data from ear, finger and forehead sites with our high-resolution time-frequency spectral (TFS) technique in(More)
Motion and noise artifacts (MNA) have been a serious obstacle in realizing the potential of Photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a statistical approach based on the computation of kurtosis and Shannon Entropy (SE) for the accurate detection of MNA in PPG data. The MNA detection algorithm was verified on(More)
There has been a growing interest in out-of-center sleep testing with portable devices for accurate diagnosis of sleep apnea syndrome. This paper presents a new algorithm that extracts features based on filtering and statistical dispersion of the nasal airflow respiration signal and detects apnea events on a per-second basis. The data records were randomly(More)
Stress management is essential in this modern civilization to maintain one's stress level low and reduce health risks, since stress is one of the primary causes leading to major chronic health disorders. The present study investigates the validity of stress index (SI) metric that objectively quantifies the psychological acute stress using a disposable(More)
Polysomnography (PSG) is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This study presents an algorithm that automatically estimates the AHI value using a disposable HealthPatch(TM) sensor. Volunteers (n=53, AHI: 0.1-85.8) participated in an overnight PSG study with patch(More)
— Traditional systems for energy expenditure (EE) assessment are impractical for continuous monitoring in free-living conditions. The study presents the performance of a chest-worn wireless HealthPatch R sensor for the continuous estimation of EE rate and total energy expenditure (TEE) based on the heart rate and acceleration signals of upper torso.(More)
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