This paper presents a novel real-time QRS detection algorithm designed based on a simple moving average filter. The proposed algorithm demands no redundant preprocessing step, thus allowing a simple architecture for its implementation as well as low computational cost. Algorithm performance was validated against a subset of the MIT-BIH arrhythmia database. Consequently, numerical results showed that the proposed algorithm correctly detected over 99.5% of the QRS complexes from the standard ECG database, implying it may be considered as a simple and reliable candidate of QRS detection algorithms.

In this paper, we describe a new approach for the discrimination among ventricular fibrillation (VF), ventricular tachycardia (VT) and superventricular tachycardia (SVT) developed using a total least squares (TLS)-based Prony modeling algorithm. Two features, dubbed energy fractional factor (EFF) and predominant frequency (PF), are both derived from the TLS-based Prony model. In general, EFF is adopted for discriminating SVT from ventricular tachyarrhythmias (i.e., VF and VT) first, and PF is then used for further separation of VF and VT. Overall classification is achieved by performing a two-stage process to the indicators defined by EFF and PF values, respectively. Tests conducted using 91 episodes drawn from the MIT-BIH database produced optimal predictive accuracy of (SVT, VF, VT) = (95.24%, 96.00%, 97.78%). A data decimation process is also introduced in the novel method to enhance the computational efficiency, resulting in a significant reduction in the time required for generating the feature values.

In this paper, a discrete wavelet transform (DWT) based de-noising with its applications into the noise reduction for medical signal preprocessing is introduced. This work focuses on the hardware realization of a real-time wavelet de-noising procedure. The proposed de-noising circuit mainly consists of three modules: a DWT, a thresholding, and an inverse DWT (IDWT) modular circuits. We also proposed a novel adaptive thresholding scheme and incorporated it into our wavelet de-noising procedure. Performance was then evaluated on both the architectural designs of the software and. In addition, the de-noising circuit was also implemented by downloading the Verilog codes to a field programmable gate array (FPGA) based platform so that its ability in noise reduction may be further validated in actual practice. Simulation experiment results produced by applying a set of simulated noise-contaminated electrocardiogram (ECG) signals into the de-noising circuit showed that the circuit could not only desirably meet the requirement of real-time processing, but also achieve satisfactory performance for noise reduction, while the sharp features of the ECG signals can be well preserved. The proposed de-noising circuit was further synthesized using the Synopsys Design Compiler with an Artisan Taiwan Semiconductor Manufacturing Company (TSMC, Hsinchu, Taiwan) 40 nm standard cell library. The integrated circuit (IC) synthesis simulation results showed that the proposed design can achieve a clock frequency of 200 MHz and the power consumption was only 17.4 mW, when operated at 200 MHz.

A real-time muscle fatigue monitoring system was developed to quantitatively detect the muscle fatigue of subjects during cycling movement, where a fatigue progression measure (FPM) was built-in. During the cycling movement, the electromyogram (EMG) signals of the vastus lateralis and gastrocnemius muscles in one leg as well as cycling speed are synchronously measured in a real-time fashion. In addition, the heart rate (HR) and the Borg rating of perceived exertion scale value are recorded per minute. Using the EMG signals, the electrical activity and median frequency (MF) are calculated per cycle. Moreover, the updated FPM, based on the percentage of reduced MF counts during cycling movement, is calculated to measure the onset time and the progressive process of muscle fatigue. To demonstrate the performance of our system, five young healthy subjects were recruited. Each subject was asked to maintain a fixed speed of 60 RPM, as best he/she could, under a constant load during the pedaling. When the speed reached 20 RPM or the HR reached the maximal training HR, the experiment was then terminated immediately. The experimental results show that the proposed system may provide an on-line fatigue monitoring and analysis for the lower extremity muscles during cycling movement.