Search Results (6)

Distortions of electrocardiograms (ECGs) caused by mandatory high-pass and low-pass analog RC filters in ECG devices are always present. The fidelity of the ECG waveform requires limiting the RC cutoff frequencies of the diagnostic (0.05–150 Hz) and monitoring systems (0.5–40 Hz). However, the use of fixed frequency bands is a compromise between enhanced noise immunity and ECG distortions. This study aims to propose active inverse high-pass and low-pass filters which are able to compensate for distortions in digital recordings of RC-filtered ECGs, thereby overcoming the limitations imposed by analog filtering. A new straightforward design of an inverse high-pass filter (IHPF) uses an integrator as the forward-path gain block, with a feedback loop containing an active digital filter equivalent to the analog RC high-pass filter. In contrast, the inverse low-pass filter (ILPF) employs a constant-gain block in the forward path to ensure stability and prevent phase delay, while its feedback path features an active digital counterpart of the RC low-pass filter. Second-order inverse filters are created by cascading two first-order stages. The proposed filters were validated according to essential performance requirements for electrocardiographs. The low-frequency (impulse) responses of IHPFs with cutoff frequencies of 0.05–5 Hz exhibit no overshoot and undershoot by magnitudes of 0.1–25 µV, well within the ±100 µV compliance limit defined for a test rectangular pulse (3 mV, 100 ms). The high-frequency responses of ILPFs with cutoff frequencies of 10–150 Hz present a relative amplitude drop of only 0.2–2.5%, far below the 10% limit for peak amplitude reduction of a triangular pulse (1.5 mV) with 20 ms vs. 200 ms widths. For any of the eight ECG leads (I, II, and V1–V6) available in the standard signal (ANE20000), the IHPF (0.05–5 Hz) presents ST-segment deviations <5 μV (within the ±25 μV limit) and R- and S-peak deviations <±3.5% (within the ±5% limit). The ILPF (10–150 Hz) preserves R- and S-peak amplitudes with deviations less than −1%. Diagnostic-level recovery of ECG waveforms distorted by first- and second-order analog RC filters in ECG devices is possible with the innovative and comprehensive inverse filter design presented in this study. This approach offers a significant advancement in ECG signal processing, effectively restoring essential waveform components even after aggressive, noise-robust analog filtering in ECG acquisition circuits. Although validated for ECG signals, the proposed inverse filters are also applicable to other biosignal front-end circuits employing RC coupling. Full article

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed to ensure consistency in amplitude, rather than solely in shape, for digital signals. The original signal reconstruction algorithm was validated with the MIT–BIH and PTB databases by comparing the difference between the reconstructed and the original signals. Moreover, the mean of the Pearson correlation coefficient was 0.97 and 0.98, respectively, while the mean absolute errors were 0.324 and 0.241, respectively. The method proposed in this study converts paper-recorded ECGs into a digital format, enabling direct analysis using software. Automated techniques for acquiring and restoring ECG reference voltages enhance the reconstruction accuracy. This innovative approach facilitates data storage, medical communication, and remote ECG analysis, and minimizes errors in remote diagnosis. Full article

Sudden Cardiac Arrest (SCA) is one of the leading causes of death worldwide. Therefore, timely and accurate detection of such arrests and immediate defibrillation support for the victim is critical. An automated external defibrillator (AED) is a medical device that diagnoses the rhythms and provides electric shocks to SCA patients to restore normal heart rhythms. Machine learning and deep learning-based approaches are popular in AEDs for detecting shockable rhythms and automating defibrillation. There are some works in the literature for reviewing various machine learning (ML) and deep learning (DL) algorithms for shockable ECG signals in AED. Starting in 2017 and beyond, different DL algorithms were proposed for the AED. This paper provides an overview of AED, including its circuit diagram and application to SCA patients. It also presents the most up-to-date ML and DL approaches for detecting shockable rhythms in AEDs without cardiopulmonary resuscitation (CPR) or during CPR. It also provides a performance comparison of these approaches and discusses other researchers’ results that lay the foundation for researchers to delve in-depth. Furthermore, the research gaps and recommendations for future research provided in this review paper will be helpful to the researchers, scientists, and engineers in conducting further research in this critical field. Full article

In the past few years, deep learning-based electrocardiogram (ECG) compression methods have achieved high-ratio compression by reducing hidden nodes. However, this reduction can result in severe information loss, which will lead to poor quality of the reconstructed signal. To overcome this problem, a novel quality-guaranteed ECG compression method based on a binary convolutional auto-encoder (BCAE) equipped with residual error compensation (REC) was proposed. In traditional compression methods, ECG signals are compressed into floating-point numbers. BCAE directly compresses the ECG signal into binary codes rather than floating-point numbers, whereas binary codes take up fewer bits than floating-point numbers. Compared with the traditional floating-point number compression method, the hidden nodes of the BCAE network can be artificially increased without reducing the compression ratio, and as many hidden nodes as possible can ensure the quality of the reconstructed signal. Furthermore, a novel optimization method named REC was developed. It was used to compensate for the residual between the ECG signal output by BCAE and the original signal. Complemented with the residual error, the restoration of the compression signal was improved, so the reconstructed signal was closer to the original signal. Control experiments were conducted to verify the effectiveness of this novel method. Validated by the MIT-BIH database, the compression ratio was 117.33 and the root mean square difference (PRD) was 7.76%. Furthermore, a portable compression device was designed based on the proposed algorithm using Raspberry Pi. It indicated that this method has attractive prospects in telemedicine and portable ECG monitoring systems. Full article

Patients with central respiratory paralysis can benefit from diaphragm pacing to restore respiratory function. However, it would be important to develop a continuous respiratory monitoring method to alert on apnea occurrence, in order to improve the efficiency and safety of the pacing system. In this study, we present a preliminary validation of an acoustic apnea detection method on healthy subjects data. Thirteen healthy participants performed one session of two 2-min recordings, including a voluntary respiratory pause. The recordings were post-processed by combining temporal and frequency detection domains, and a new method was proposed—Phonocardiogram-Derived Respiration (PDR). The detection results were compared to synchronized pneumotachograph, electrocardiogram (ECG), and abdominal strap (plethysmograph) signals. The proposed method reached an apnea detection rate of 92.3%, with 99.36% specificity, 85.27% sensitivity, and 91.49% accuracy. PDR method showed a good correlation of 0.77 with ECG-Derived Respiration (EDR). The comparison of R-R intervals and S-S intervals also indicated a good correlation of 0.89. The performance of this respiratory detection algorithm meets the minimal requirements to make it usable in a real situation. Noises from the participant by speaking or from the environment had little influence on the detection result, as well as body position. The high correlation between PDR and EDR indicates the feasibility of monitoring respiration with PDR. Full article

In this paper, a packet generator using a pattern matching algorithm for real-time abnormal heartbeat detection is proposed. The packet generator creates a very small data packet which conveys sufficient crucial information for health condition analysis. The data packet envelopes real time ECG signals and transmits them to a smartphone via Bluetooth. An Android application was developed specifically to decode the packet and extract ECG information for health condition analysis. Several graphical presentations are displayed and shown on the smartphone. We evaluate the performance of abnormal heartbeat detection accuracy using the MIT/BIH Arrhythmia Database and real time experiments. The experimental result confirm our finding that abnormal heart beat detection is practically possible. We also performed data compression ratio and signal restoration performance evaluations to establish the usefulness of the proposed packet generator and the results were excellent. Full article