Ming-Zher Poh

Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.

Remote measurements of the cardiac pulse can provide comfortable physiological assessment without electrodes. However, attempts so far are non-automated, susceptible to motion artifacts and typically expensive. In this paper, we introduce a new methodology that overcomes these problems. This novel approach can be applied to color video recordings of the human face and is based on automatic face tracking along with blind source separation of the color channels into independent components. Using Bland-Altman and correlation analysis, we compared the cardiac pulse rate extracted from videos recorded by a basic webcam to an FDA-approved finger blood volume pulse (BVP) sensor and achieved high accuracy and correlation even in the presence of movement artifacts. Furthermore, we applied this technique to perform heart rate measurements from three participants simultaneously. This is the first demonstration of a low-cost accurate video-based method for contact-free heart rate measurements that is automated, motion-tolerant and capable of performing concomitant measurements on more than one person at a time.

Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam

We present a simple, low-cost method for measuring multiple physiological parameters using a basic webcam. By applying independent component analysis on the color channels in video recordings, we extracted the blood volume pulse from the facial regions. Heart rate (HR), respiratory rate, and HR variability (HRV, an index for cardiac autonomic activity) were subsequently quantified and compared to corresponding measurements using Food and Drug Administration-approved sensors. High degrees of agreement were achieved between the measurements across all physiological parameters. This technology has significant potential for advancing personal health care and telemedicine.

iCalm: Wearable Sensor and Network Architecture for Wirelessly Communicating and Logging Autonomic Activity

Widespread use of affective sensing in healthcare applications has been limited due to several practical factors, such as lack of comfortable wearable sensors, lack of wireless standards, and lack of low-power affordable hardware. In this paper, we present a new low-cost, low-power wireless sensor platform implemented using the IEEE 802.15.4 wireless standard, and describe the design of compact wearable sensors for long-term measurement of electrodermal activity, temperature, motor activity, and photoplethysmography. We also illustrate the use of this new technology for continuous long-term monitoring of autonomic nervous system and motion data from active infants, children, and adults. We describe several new applications enabled by this system, discuss two specific wearable designs for the wrist and foot, and present sample data.

Autonomic changes with seizures correlate with postictal EEG suppression

Objective: Sudden unexpected death in epilepsy (SUDEP) poses a poorly understood but considerable risk to people with uncontrolled epilepsy. There is controversy regarding the significance of postictal generalized EEG suppression as a biomarker for SUDEP risk, and it remains unknown whether postictal EEG suppression has a neurologic correlate. Here, we examined the profile of autonomic alterations accompanying seizures with a wrist-worn biosensor and explored the relationship between autonomic dysregulation and postictal EEG suppression. Methods: We used custom-built wrist-worn sensors to continuously record the sympathetically mediated electrodermal activity (EDA) of patients with refractory epilepsy admitted to the long-term video-EEG monitoring unit. Parasympathetic-modulated high-frequency (HF) power of heart rate variability was measured from concurrent EKG recordings. Results: A total of 34 seizures comprising 22 complex partial and 12 tonic-clonic seizures from 11 patients were analyzed. The postictal period was characterized by a surge in EDA and heightened heart rate coinciding with persistent suppression of HF power. An increase in the EDA response amplitude correlated with an increase in the duration of EEG suppression (r = 0.81, p = 0.003). Decreased HF power correlated with an increase in the duration of EEG suppression (r = −0.87, p = 0.002). Conclusion: The magnitude of both sympathetic activation and parasympathetic suppression increases with duration of EEG suppression after tonic-clonic seizures. These results provide autonomic correlates of postictal EEG suppression and highlight a critical window of postictal autonomic dysregulation that may be relevant in the pathogenesis of SUDEP.

Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography

This paper addresses the design considerations and critical evaluation of a novel embodiment for wearable photoplethysmography (PPG) comprising a magnetic earring sensor and wireless earpiece. The miniaturized sensor can be worn comfortably on the earlobe and contains an embedded accelerometer to provide motion reference for adaptive noise cancellation. The compact wireless earpiece provides analog signal conditioning and acts as a data-forwarding device via a radio frequency transceiver. Using Bland-Altman and correlation analysis, we evaluated the performance of the proposed system against an FDA-approved ECG measurement device during daily activities. The mean ± standard deviation (SD) of the differences between heart rate measurements from the proposed device and ECG (expressed as percentage of the average between the two techniques) along with the 95% limits of agreement (LOA = ±1.96 SD) was 0.62% ± 4.51% (LOA = -8.23% and 9.46%), -0.49% ± 8.65% (-17.39% and 16.42%), and -0.32% ± 10.63% (-21.15% and 20.52%) during standing, walking, and running, respectively. Linear regression indicated a high correlation between the two measurements across the three evaluated conditions (r = 0.97, 0.82, and 0.76, respectively with p < 0.001). The new earring PPG system provides a platform for comfortable, robust, unobtrusive, and discreet monitoring of cardiovascular function.

Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor

The special requirements for a seizure detector suitable for everyday use in terms of cost, comfort, and social acceptance call for alternatives to electroencephalography (EEG)–based methods. Therefore, we developed an algorithm for automatic detection of generalized tonic–clonic (GTC) seizures based on sympathetically mediated electrodermal activity (EDA) and accelerometry measured using a novel wrist‐worn biosensor. The problem of GTC seizure detection was posed as a supervised learning task in which the goal was to classify 10‐s epochs as a seizure or nonseizure event based on 19 extracted features from EDA and accelerometry recordings using a Support Vector Machine. Performance was evaluated using a double cross‐validation method. The new seizure detection algorithm was tested on >4,213 h of recordings from 80 patients and detected 15 (94%) of 16 of the GTC seizures from seven patients with 130 false alarms (0.74 per 24 h). This algorithm can potentially provide a convulsive seizure alarm system for caregivers and objective quantification of seizure frequency.

Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor

We present a novel method for monitoring sympathetic nervous system activity during epileptic seizures using a wearable sensor measuring electrodermal activity (EDA). The wearable sensor enables long-term, continuous EDA recordings from patients. Preliminary results from our pilot study suggest that epileptic seizures induce a surge in EDA. These changes are greater in generalized tonic-clonic seizures and reflect a massive sympathetic discharge. This paper offers a new approach for investigating the relationship between epileptic seizures and autonomic alterations.

Heartphones: Sensor Earphones and Mobile Application for Non-obtrusive Health Monitoring

This paper describes the development and evaluation of wearable sensor earphones coupled with a mobile platform for comfortable assessment of cardiovascular function. Photoplethysmographic sensors were integrated into regular earphones to provide discreet and non-stigmatizing heart rate measurements. Preliminary results indicate that the proposed system has a high degree of accuracy at rest with an average error of 0.63%.

Diagnostic Performance of a Smartphone-Based Photoplethysmographic Application for Atrial Fibrillation Screening in a Primary Care Setting.

Background Diagnosing atrial fibrillation (AF) before ischemic stroke occurs is a priority for stroke prevention in AF. Smartphone camera–based photoplethysmographic (PPG) pulse waveform measurement discriminates between different heart rhythms, but its ability to diagnose AF in real‐world situations has not been adequately investigated. We sought to assess the diagnostic performance of a standalone smartphone PPG application, Cardiio Rhythm, for AF screening in primary care setting. Methods and Results Patients with hypertension, with diabetes mellitus, and/or aged ≥65 years were recruited. A single‐lead ECG was recorded by using the AliveCor heart monitor with tracings reviewed subsequently by 2 cardiologists to provide the reference standard. PPG measurements were performed by using the Cardiio Rhythm smartphone application. AF was diagnosed in 28 (2.76%) of 1013 participants. The diagnostic sensitivity of the Cardiio Rhythm for AF detection was 92.9% (95% CI] 77–99%) and was higher than that of the AliveCor automated algorithm (71.4% [95% CI 51–87%]). The specificities of Cardiio Rhythm and the AliveCor automated algorithm were comparable (97.7% [95% CI: 97–99%] versus 99.4% [95% CI 99–100%]). The positive predictive value of the Cardiio Rhythm was lower than that of the AliveCor automated algorithm (53.1% [95% CI 38–67%] versus 76.9% [95% CI 56–91%]); both had a very high negative predictive value (99.8% [95% CI 99–100%] versus 99.2% [95% CI 98–100%]). Conclusions The Cardiio Rhythm smartphone PPG application provides an accurate and reliable means to detect AF in patients at risk of developing AF and has the potential to enable population‐based screening for AF.

Cardiovascular Monitoring Using Earphones and a Mobile Device

Many wearable biosensors have failed to be adopted outside of a lab setting or to gain popular acceptance. The Heartphones project seeks to address this by integrating physiological sensing capabilities into a platform already accepted for everyday use, exploiting sensor-embedded earphones and a smartphone.