An Efficient QRS Complex Detection Using Optimally Designed Digital Differentiator

In this paper, a computationally efficient, highly accurate, wideband, stable, and minimum phase infinite impulse response type first‐order digital differentiator (DD) is designed by employing a swarm intelligence‐based search method called Salp Swarm Algorithm (SSA) for the QRS complex detection application. The optimal coefficients of the DD are computed by minimizing a suitable fitness function to meet the ideal differentiator magnitude response characteristics. The simulation results and the root mean square magnitude error metric justify the superiority of the proposed SSA‐based DD design as compared with all other differentiators employed in the QRS complex detection application, and the reported first‐order DDs based on the numerical methods and the other evolutionary algorithms. The electrocardiogram signal is preprocessed by the proposed DD to generate the feature signal corresponding to each QRS complex. The generated feature signal is used as a marker to identify the exact occurrence of the QRS complex by using an adaptive threshold‐based detection logic. The proposed DD‐based QRS detection approach achieves a sensitivity (Se), positive prediction (PP), detection error rate (DER), and accuracy (Acc) of 99.94%, 99.93%, 0.1279%, and 99.87%, respectively, when validated against MIT/BIH arrhythmia database. Also, against the QT database, the proposed QRS detector produces a Se of 99.93%, PP of 99.97%, DER of 0.09%, and Acc of 99.90%. The performance of the proposed QRS detection technique is compared with the methods already reported in the recent literature, and the superiority of the proposed approach is established with respect to different standard performance metrics. The noise tolerance capability of the proposed QRS detector is demonstrated against MIT/BIH noise stress test database.

The analyses of electrocardiogram (ECG) and heart rate variability (HRV) are of primordial interest for cardiovascular diseases. The algorithm used for the detection of the QRS complex is the basis for HRV analysis and HRV quality will depend strongly on it. The aim of this paper is to implement HRV analysis in real time on an ARM microcontroller (MCU). Thus, there is no need to send raw data to a cloud server for real time HRV monitoring and, consequently, the communication requirements and the power consumption of the local sensor node would be far lower. The system would facilitate the integration into edge computing, for instance, in small local networks, such as hospitals. A QRS detector based on wavelets is proposed, which is able to autonomously select the coefficients the QRS complex will be detected with. To validate it, the MITBIH and NSRDB databases were used. This detector was implemented in real time using an MCU. Subsequently HRV analysis was implemented in the time, frequency, and nonlinear domains. When evaluating the QRS detector with the MITBIH database, 99.61% positive prediction (PP), 99.3% sensitivity (SE), and a prediction error rate (DER) of 1.12% were obtained. For the NSRDB database the results were a PP of 99.95%, an SE of 99.98%, and a DER of 0.0006%. The execution of the QRS detector in the MCU took 52 milliseconds. On the other hand, the time required to calculate the HRV depends on the data size, but it took only a few seconds to analyze several thousands of interbeat intervals. The results obtained for the detector were superior to 99%, so it is expected that the HRV is reliable. It has also been shown that the detection of QRS complex can be done in real time using advanced processing techniques such as wavelets.

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature.

Signal processing has become a core technology in medical devices today, as it is crucial in a wide range of applications. It is used to automate the measurement of various signal characteristics, which previously was done manually and, as a result, subjectivity is reduced and reliability is increased. Another purpose is to filter out undesired signal components of technical or physiological origin so that the signalto-noise ratio is improved and subsequent analysis facilitated. Signal processing is also of key importance when uncovering components with low amplitude and/or subtle variations in frequency, which are very difficult, if not impossible, to observe by the naked eye. For example, signal processing plays a crucial role for the detection of the alternans phenomenon occurring during cardiac repolarization. Information about alternans has turned out to be the most useful for risk stratification of certain cardiac patients. The analysis of heart rate variability represents another application where signal processing is essential for the characterization of tiny oscillations in rhythm; the resulting measurements convey unique information about the autonomic nervous system. Considering that today’s recording equipment and computers are quite powerful and available at affordable prices, limitations related to implementation are rather procedural than computational. The role of signal processing can therefore be expected to further increase in the future as novel techniques are developed and used to uncover and characterize hidden activity which may be present in a signal. Traditionally, biomedical signal processing has been largely synonymous to ‘unimodal’ analysis in which only one type of signal has been subjected to analysis at a time. Countless approaches have been developed to unimodal analysis of the most common bioelectrical signals, i.e. the electrocardiogram (ECG), the electroencephalogram (EEG) and the electromyogram (EMG). However, multimodal signal modelling and processing are currently receiving considerable attention as it is highly desirable to account for the interdependence that exists between different

Accurate processing of electrocardiogram (ECG) signals requires a sensitive and robust QRS detection method. In this study, three methods are quantitatively compared using a similar algorithm structure but applying different transforms to the differentiated ECG. The three transforms used are the Hilbert transformer, the squaring function, and a second discrete derivative stage. The first two have been widely used in ECG and heart rate variability analysis while the second derivative method aims to explain the success of the Hilbert transform. The algorithms were compared in terms of the number of false positive and false negative detections produced for records of the MIT/BIH Arrhythmia Database. The Hilbert transformer and the squaring function both produced a sensitivity and positive predictivity of over 99%, though the squaring function had a lower overall detection error rate. The second derivative resulted in the highest overall detection error rate. Different algorithms performed better for diverse ECG characteristics; suggesting that an algorithm can be specified for different recordings, the algorithms can be combined based on each one's characteristics to determine a new more accurate method, or an additional detection stage can be added to reduce the number of false negatives

Accurate processing of electrocardiogram (ECG) signals requires a sensitive and robust QRS detection method. In this study, three methods are quantitatively compared using a similar algorithm structure but applying different transforms to the differentiated ECG. The three transforms used are the Hilbert transformer, the squaring function, and a second discrete derivative stage. The first two have been widely used in ECG and heart rate variability analysis while the second derivative method aims to explain the success of the Hilbert transform. The algorithms were compared in terms of the number of false positive and false negative detections produced for records of the MIT/BIH Arrhythmia Database. The Hilbert transformer and the squaring function both produced a sensitivity and positive predictivity of over 99%, though the squaring function had a lower overall detection error rate. The second derivative resulted in the highest overall detection error rate. Different algorithms performed better for diverse ECG characteristics; suggesting that an algorithm can be specified for different recordings, the algorithms can be combined based on each one's characteristics to determine a new more accurate method, or an additional detection stage can be added to reduce the number of false negatives.

This study investigates the effects of using a large data set on supervised machine learning classifiers in the domain of Intrusion Detection Systems (IDS). To investigate this effect 12 machine learning algorithms have been applied. These algorithms are: (1) Adaboost, (2) Bayesian Nets, (3) Decision Tables, (4) Decision Trees (J48), (5)Logistic Regression, (6) Multi-Layer Perceptron, (7) Naive Bayes, (8) OneRule, (9)Random Forests, (10) Radial Basis Function Neural Networks, (11) Support Vector Machines (two different training algorithms), and (12) ZeroR. A well-known IDS benchmark dataset, KDD99 has been used to train and test classifiers. Full training data set of KDD99 is 4.9 million instances while full test dataset is 311,000 instances. In contrast to similar previous studies, which used 0.08%–10% for training and 1.2%–100% for testing, this study uses full training dataset and full test dataset. Weka Machine Learning Toolbox has been used for modeling and simulation. The performance of classifiers has been evaluated using standard binary performance metrics: Detection Rate, True Positive Rate, True Negative Rate, False Positive Rate, False Negative Rate, Precision, and F1-Rate. To show effects of dataset size, performance of classifiers has been also evaluated using following hardware metrics: Training Time, Working Memory and Model Size. Test results shows improvements in classifiers in standard performance metrics compared to previous studies.

Driver Drowsiness Detection Study using Heart Rate Variability analysis in Virtual Reality Environment

An optimally designed digital differentiator based preprocessor for R-peak detection in electrocardiogram signal

Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of [Formula: see text] waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.

QRS complex detection in ECG signals using locally adaptive weighted total variation denoising

The heart rate variability (HRV) signal is indicative of autonomic regulation of the heart rate (HR). It could be used as a noninvasive marker in monitoring the physiological state of an individual. Currently, the primary method of deriving the HRV signal is to acquire the electrocardiogram (ECG) signal, apply appropriate QRS detection algorithms to locate the R wave and its peak, find the RR intervals, and perform suitable interpolation and resampling to produce a uniformly sampled tachogram. This process could sometimes result in errors in the HRV signal due to drift, electromagnetic and biologic interference, and the complex morphology of the ECG signal. The photoplethysmographic (PPG) signal has the potential to eliminate the problems with the ECG signal to derive the HRV signal. To investigate this point, a PDA-based system was developed to simultaneously record ECG and PPG signals to facilitate accurately controlled sampling and recording durations. Two healthy young volunteers participated in this pilot study to evaluate the applicability of our approach. To improve data quality, ECG and PPG recordings were acquired three times/subject. A comparison between different features of the HRV signals derived from both methods was performed to test the validity of using PPG signals in HRV analysis. We used autoregressive (AR) modeling, Poincare' plots, cross correlation, standard deviation, arithmetic mean, skewness, kurtosis, and approximate entropy (ApEn) to derive and compare different measures from both ECG and PPG signals. This study demonstrated that our PDA-based system was a convenient and reliable means for acquisition of PPG-derived and ECG-derived HRV signals. The excellent agreement between different measures of HRV signals acquired from both methods provides potential support for the idea of using PPGs instead of ECGs in HRV signal derivation and analysis in ambulatory cardiac monitoring of healthy individuals.

Long-term heart rate variability (HRV) analysis is useful as a noninvasive technique for autonomic nervous system activity assessment. It provides a method for assessing many physiological and pathological factors that modulate the normal heartbeat. The performance of HRV analysis systems heavily depends on a reliable and accurate detection of the R peak of the QRS complex. Ectopic beats caused by misdetection or arrhythmic events can introduce bias into HRV results, resulting in significant problems in their interpretation. This study presents a novel method for long-term detection of normal R peaks (which represent the normal heartbeat in electrocardiographic signals), intended specifically for HRV analysis. The very low computational complexity of the proposed method, which combines and exploits the advantages of syntactical and statistical approaches, enables real-time applications. The approach was validated using the Massachusetts Institute of Technology–Beth Israel Hospital Normal Sinus Rhythm and the Fantasia database, and has a sensitivity, positive predictivity, detection error rate, and accuracy of 99.998, 99.999, 0.003, and 99.996%, respectively.

Heart rate variability (HRV) analysis of electrocardiogram (ECG) signal is used to examine the health condition of the sportsperson. For the present work, a total of 22 numbers of data were taken from eleven participants in two different physical conditions who are under regular sporting activity. One set of data were taken just after playing badminton and 400-600 m run and another set of ECG data were taken in the normal i.e., relaxed condition. Acquired ECG signal is pre-processed for removal of powerline frequency, baseline wander (BW) removal, removal of low-frequency noise. The smoothening filter is also used for removal of high-frequency noise for smoothing of the wave. The filtered signals are used for HRV analysis. HRV is associated with average heart rate (HR) tachycardia and bradycardia. Main HRV parameters like mean heart rate, mean RR-interval, SDNN, RMSSD, NN50, pNN50 are analyzed in two different conditions namely post exercise and relaxed condition. In addition to HRV, the complexity analysis of the signal is also being done in the contest of sample entropy, approximate entropy etc.

Unsupervised anomaly detection in time-series has been extensively investigated in the literature. Notwithstanding the relevance of this topic in numerous application fields, a comprehensive and extensive evaluation of recent state-of-the-art techniques taking into account real-world constraints is still needed. Some efforts have been made to compare existing unsupervised time-series anomaly detection methods rigorously. However, only standard performance metrics, namely precision, recall, and F1-score are usually considered. Essential aspects for assessing their practical relevance are therefore neglected. This paper proposes an in-depth evaluation study of recent unsupervised anomaly detection techniques in time-series. Instead of relying solely on standard performance metrics, additional yet informative metrics and protocols are taken into account. In particular, (i) more elaborate performance metrics specifically tailored for time-series are used; (ii) the model size and the model stability are studied; (iii) an analysis of the tested approaches with respect to the anomaly type is provided; and (iv) a clear and unique protocol is followed for all experiments. Overall, this extensive analysis aims to assess the maturity of state-of-the-art time-series anomaly detection, give insights regarding their applicability under real-world setups and provide to the community a more complete evaluation protocol.