Related papers: An ECG-SoC with 535nW/channel lossless data compre…
Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption,…
Cloud computing for storing data and running complex algorithms have been steadily increasing. As connected IoT devices such as wearable ECG recorders generally have less storage and computational capacity, acquired signals get sent to a…
We proposed a practical ECG compression system which is beneficial for tele-monitoring cardiovascular diseases. There are two steps in the compression framework. First, we partition ECG signal into segments according to R- to R-wave…
Recently, it has been shown that compressed sensing (CS) has the potential to lower energy consumption in wireless electrocardiogram (ECG) systems. By reducing the number of acquired measurements, the communication burden is decreased and…
This paper proposes a low-cost and highly accurate ECG-monitoring system intended for personalized early arrhythmia detection for wearable mobile sensors. Earlier supervised approaches for personalized ECG monitoring require both abnormal…
Since cadiovascular disease (CVD) posts a heavy threat to people's health, long-term electrocardiogram (ECG) monitoring is of great value for the improvement of treatment. To realize remote long-term ECG monitoring, a low-power wireless…
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization…
Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise…
Connected health is a technology that associates medical devices, security devices and communication technologies. It enables patients to be monitored and treated remotely from their home. Patients' data and medical records within a…
The electrocardiogram (ECG) signal is one of the most vital signals that can be used to investigate the performance of heart. Based on the ECG graph, we can identify different disorders and diseases. Therefore, monitoring this signal is of…
This paper describes a highly-integrated CMOS system-on-chip (SoC) for active structural health monitoring (SHM). The chip integrates ultrasonic power and bidirectional half-duplex data transfer, a power management unit (PMU), and an…
Electrocardiogram (ECG) signals are often degraded by various noise sources such as baseline wander, motion artifacts, and electromyographic interference, posing a major challenge in clinical settings. This paper presents a lightweight deep…
Continuous and long term acquisition of multi-channel ECG measurements are significant for diagnostic purposes. Compressive sensing has been proposed in the literature for obtaining continuous ECG measurements as it provides advantages…
This project introduces a groundbreaking approach to address the challenge of periodic signal compression. By proposing a novel adaptive coding method, coupled with hardware-assisted data compression, we have developed a new architecture…
A low-power wearable wireless sensor measuring both molecular biomarkers and physiological signals is proposed, where the former are measured by a microfluidic biosensing system while the latter are measured electrically. The low-power…
Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and…
Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. In this paper, we propose a data-driven CS framework that learns signal characteristics and individual…
Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms that provide an accurate classification of bio-signals while consuming low average power for long-term battery-operated use. Single lead…
Activity recognition, as an important component of behavioral monitoring and intervention, has attracted enormous attention, especially in Mobile Cloud Computing (MCC) and Remote Health Monitoring (RHM) paradigms. While recently resource…
Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint…