A Low-Latency, Low-Power FPGA Implementation of ECG Signal Characterization Using Hermite Polynomials

Automatic ECG signal characterization is of critical importance in patient monitoring and diagnosis. This process is computationally intensive, and low-power, online (real-time) solutions to this problem are of great interest. In this paper, we present a novel, dedicated hardware implementation of the ECG signal processing chain based on Hermite functions, aiming for real-time processing. Starting from 12-bit ADC samples of the ECG signal, the hardware implements filtering, peak and QRS detection, and least-squares Hermite polynomial fit on heartbeats. This hardware module can be used to compress ECG data or to perform beat classification. The hardware implementation has been validated on a Field Programmable Gate Array (FPGA). The implementation is generated using an algorithm-to-hardware compiler tool-chain and the resulting hardware is characterized using a low-cost off-the-shelf FPGA card. The single-beat best-fit computation latency when using six Hermite basis polynomials is under 1 s with a throughput of 3 beats/s and with an average power dissipation around 28 mW, demonstrating true real-time applicability.

Investigation of heart rate variability of patients undergoing coronary artery bypass grafting using the statistical signal characterization method

BACKGROUND: Autonomic function can be estimated non-invasively using heart rate variability (HRV). HRV of patients undergoing coronary artery bypass grafting (CABG) is investigated in time-domain and frequency-domain before and after CABG to study the effect of operation on the status of patients. OBJECTIVE: The main purpose of this work is to evaluate the effect of CABG surgery on patients with ischemic heart disease (IHD) before operation, and to monitor the status of patients on day 6 and day 30 after the CABG operation. METHODS: The statistical signal characterization (SSC) technique is used in this work in order to derive different morphology-based parameters to indirectly describe time-domain and frequency-domain HRV parameters in 24 patients undergoing CABG operation, before the operation (Group 1: G1), 6 days after operation (Group 2: G2) and 30 days after operation (Group 3: G3). The data is obtained from the Sultan Qaboos University Hospital in Oman. RESULTS: The SSC parameters Mean(mt) and Mean(dt) are reduced in all 24 patients and in 23 out of 24 patients in G2 compared to G1 (6-days after operation compared with before operation), respectively. Comparing G3 to G1 the reduction in Mean(mt) and Mean(dt) is noted in 18 of the 24 patients. CONCLUSIONS: The parameters Mean(mt) and Mean(dt) are successful parameters to follow the HRV for patients undergoing CABG surgery. A relation between those SSC parameters and the HRV time-domain and frequency-domain parameters is investigated in this paper to understand the physiological behavior of the patients.

Bispectral Analysis of Heart Rate Variability to Characterize and Help Diagnose Pediatric Sleep Apnea

Pediatric obstructive sleep apnea (OSA) is a breathing disorder that alters heart rate variability (HRV) dynamics during sleep. HRV in children is commonly assessed through conventional spectral analysis. However, bispectral analysis provides both linearity and stationarity information and has not been applied to the assessment of HRV in pediatric OSA. Here, this work aimed to assess HRV using bispectral analysis in children with OSA for signal characterization and diagnostic purposes in two large pediatric databases (0–13 years). The first database (training set) was composed of 981 overnight ECG recordings obtained during polysomnography. The second database (test set) was a subset of the Childhood Adenotonsillectomy Trial database (757 children). We characterized three bispectral regions based on the classic HRV frequency ranges (very low frequency: 0–0.04 Hz; low frequency: 0.04–0.15 Hz; and high frequency: 0.15–0.40 Hz), as well as three OSA-specific frequency ranges obtained in recent studies (BW1: 0.001–0.005 Hz; BW2: 0.028–0.074 Hz; BWRes: a subject-adaptive respiratory region). In each region, up to 14 bispectral features were computed. The fast correlation-based filter was applied to the features obtained from the classic and OSA-specific regions, showing complementary information regarding OSA alterations in HRV. This information was then used to train multi-layer perceptron (MLP) neural networks aimed at automatically detecting pediatric OSA using three clinically defined severity classifiers. Both classic and OSA-specific MLP models showed high and similar accuracy (Acc) and areas under the receiver operating characteristic curve (AUCs) for moderate (classic regions: Acc = 81.0%, AUC = 0.774; OSA-specific regions: Acc = 81.0%, AUC = 0.791) and severe (classic regions: Acc = 91.7%, AUC = 0.847; OSA-specific regions: Acc = 89.3%, AUC = 0.841) OSA levels. Thus, the current findings highlight the usefulness of bispectral analysis on HRV to characterize and diagnose pediatric OSA.

Detection and monitoring for cancer and abnormal vasculature by photoacoustic signal characterization of structural morphology

Photoacoustic systems can produce high-resolution, high-contracts images of vascular structures. To reconstruct images at very high-resolution, signals must be collected from many transducer locations, which can be time consuming due to limitations in transducer array technology, In this thesis a method is presented to discriminate between normal and abnormal tissue based on the structural morphology of vasculature and permits data to be acquired quickly. To demonstrate that the approach may be useful for cancer detection, a special simulator that produces photoacoustic signal from 3D models of vascular tissue is developed. Validation of the simulator is performed against a derived exact equation for finite-length cylindrical photoacoustic sources and through FEM models. Results show that is possible to differentiate tissue classed even when it is not possible to resolve individual blood vessels. Performance of the algorithm remains strong as the number of transducer locations decreases and in the presence of noise.

Detection and monitoring for cancer and abnormal vasculature by photoacoustic signal characterization of structural morphology

Photoacoustic systems can produce high-resolution, high-contracts images of vascular structures. To reconstruct images at very high-resolution, signals must be collected from many transducer locations, which can be time consuming due to limitations in transducer array technology, In this thesis a method is presented to discriminate between normal and abnormal tissue based on the structural morphology of vasculature and permits data to be acquired quickly. To demonstrate that the approach may be useful for cancer detection, a special simulator that produces photoacoustic signal from 3D models of vascular tissue is developed. Validation of the simulator is performed against a derived exact equation for finite-length cylindrical photoacoustic sources and through FEM models. Results show that is possible to differentiate tissue classed even when it is not possible to resolve individual blood vessels. Performance of the algorithm remains strong as the number of transducer locations decreases and in the presence of noise.