Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

R PEAK DETERMINATION USING A WDFR ALGORITHM AND ADAPTIVE THRESHOLD

The determination of the R peak position in the ECG signal helps physicians not only to know the heart rate per minute, but also to monitor the patient’s health related to heart disease. This paper proposes a system to accurately determine the R peak position in the ECG signal. The system consists of a pre-processing block for filtering out noise using a WDFR algorithm and highlighting the amplitude of the R peak and a threshold value is calculated for determining the R peak. In this research, the MIT-BIH ECG dataset with 48 records are used for evaluation of the system. The results of the SEN, +P, DER and ACC parameters related to the system quality are 99.70%, 99.59%, 0.70% and 99.31%, respectively. The obtained performance of the proposed R peak position determination system is very high and can be applied to determine the R peak of the ECG signal measuring devices in practice

Electrocardiogram (ECG/EKG) using FPGA

FPGAs (Field Programmable Gate Arrays) are finding wide acceptance in medical systems for their ability for rapid prototyping of a concept that requires hardware/software co-design, for performing custom processing in parallel at high data rates and be programmed in the field after manufacturing. Based on the market demand, the FPGA design can be changed and no new hardware needs to be purchased as was the case with ASICs (Application Specific Integrated Circuit) and CPLDs (Complex Programmable Logic Device). Medical companies can now move over to FPGAs saving cost and delivering highly-efficient upgradable systems. ECG (Electrocardiogram) is considered to be a must have feature for a medical diagnostic imaging system. This project attempts at implementing ECG heart-rate computation in an FPGA. This project gave me exposure to hardware engineering, learning about the low level chips like Atmel UC3A3256 micro-controller on an Atmel EVK1105 board which is used as a simulator for generating the ECG signal, the operational amplifiers for amplifying and level-shifting the ECG signal, the A/D converter chip for analog to digital conversion of the ECG signal, the internal workings of FPGA, how different hardware components communicate with each other on the system and finally some signal processing to calculate the heart rate value from the ECG signal

Analysis of the high-frequency content in human qrs complexes by the continuous wavelet transform: An automatized analysis for the prediction of sudden cardiac death

Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk

Poisson relationships with applications to digital signal processing

Imperial Users onl

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W