IndexAbstractIntroductionReview of ECG signal morphologyDescription of the datasetMethodologyThe steps followed in this work can be summarized as follows:Results and discussionsConclusionAbstractElectrocardiography (ECG or EKG) is a non-invasive technique widely used for determine the condition of the human heart and detect any abnormal cardiac behavior. Computer systems for ECG analysis can help doctors early detect dangerous events such as ventricular fibrillation in patients at high cardiac risk. The first and crucial part of the automatic analysis of ECG signals is to accurately identify and measure the characteristic features of the ECG signal, that is, identify the exact location of the start and offset points of the P, QRS and T waves. In this article we propose a technique fast capable of accurately identifying these key landmarks using local windows around the R peaks. The proposed method has been tested on a standard QT database and very high accuracy, above 99%, is achieved in identifying different segments in the ECG signal. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay IntroductionAn ECG signal originates from the heart's electrical activity coordinating the contraction and relaxation of the different chambers of the heart. Analysis of the ECG signal and detection of its characteristic points can be used to identify various heart rhythm abnormalities, chest pain and other diseases. A cardiac cycle in an ECG signal includes P, QRS and T wave complexes. The field of automatic ECG analysis has become quite mature. Much work has been done previously to identify characteristic points in ECG signals. However, most of these use sophisticated and complex signal processing techniques which make them computationally expensive. In [11], Pan Tompkins proposed a method that recognizes QRS complexes using information about the slope, amplitude and width of the signal. But the double threshold technique used in this method to search for missed complexes is only useful if the heart rate is regular and cannot detect missed beats in case of abnormalities. In [12], all P, QRS and T complexes are detected using the wavelet transform method, but P and T onsets and offsets are not detected very accurately under severe influence of noise. [13] shows detection of the P wave in addition to the QRS complex using the hidden Markov model. In [14], QRS complexes are detected using moving average filters, but this methodology is not robust to false positives or false negatives. The QRS complex detection technique proposed in [15] applied first-order derivative and adaptive threshold adjustment to detect complexes and filtered out high-frequency noise using discrete wavelet transform. [16] introduces a new and fast version of the ECG delineation algorithm that uses line fitting, but is not robust against some arrhythmias where no wave is detected. The support vector machine was used for P and T wave detection in [17]. In [18] QRS complexes were clustered into different groups using self-organizing neural networks for detection. The algorithm proposed in [19] can be evaluated for both clinical and telemedicine ECG data. The work in [20] describes a complex QRS detector based on the dyadic wavelet transform. It performed well for multifaceted premature ventricular contractions, bigeminies, and couplet ribbons. [21] uses the S transform to isolate QRS complexes ethe Shannon energy to localize R waves. Detection of QRS complexes is also found in [22] which was performed using the difference equation operation. A QRS complex detector with limited hardware resources was proposed in [23]. In our paper, we aim to detect P, QRS and T complexes reliably and robustly using local windowing which provides very high detection accuracy and has O(N) computational complexity in detecting P, Q, S and T waves. This document is organized as follows. In section 2 we present a brief discussion on the anatomy of the ECG signal and its characteristic waveforms, section 3 provides a description of the dataset that was used to evaluate the proposed method. In Section 4, we discuss the methodologies and algorithms implemented in this work. The results obtained from the evaluation are reported in sections 5 and 6 with quantitative and qualitative interpretations. Finally, Section 7 concludes the article. Review of ECG signal morphology The ECG captures the direction and magnitude of electrical depolarization and repolarization generated by a person during his or her heartbeat cycle. The components of a normal ECG trace consist of multiple waveforms, each indicating an electrical event during a heartbeat. These waveforms are labeled as P wave, QRS complex and T wave as shown in Fig. 1. There is another small wave called U wave which is the successor of the T wave and may not always be observed due to its small size [2]. We ignore the U wave in this work. The P wave marks the beginning of the ECG cycle and is the first short upward movement of the ECG trace. It indicates that the atria are contracting, pumping blood into the ventricles. It is followed by the QRS complex, which normally begins with a downward deflection, referred to as Q; a larger upward deflection, a peak denoted as R; and then a descending S wave. Fig.1. Schematic diagram of a single ECG wave. The QRS complex represents ventricular depolarization and contraction. The PR interval indicates the transit time of the electrical signal from the sinoatrial node to the ventricles. The T wave is normally a modest ascending waveform representing ventricular repolarization. However, in some cases the T wave can be inverted [3]. Each of these waves has a characteristic duration. The P wave lasts approximately 80 ms. The normal PR interval in an ECG wave varies from 120 ms to 200 ms. The duration of the PR segment is between 50 ms and 120 ms. The duration of the QRS complex is approximately 80 ms to 120 ms. The duration of the ST segment is between 80 ms and 120 ms. The duration of the ST interval is 320 ms. The QT interval depends on the heart rate. Normal QT intervals are less than 450 ms for men and less than 460 ms for women, but can range from 270 ms with a heart rate of 150 beats per minute to 500 ms with a heart rate of 40 beats per minute [4 ]Dataset DescriptionSeveral databases are available for studying and analyzing ECG data. The dataset used in this paper is the QT database which contains 105 records, each lasting 15 minutes [5]. It was created by incorporating new data from patient Holter recordings into the MIT-BIH arrhythmia database, the European Society of Cardiology ST-T database, and several other databases [6-7]. The sampling rate of all records in this database is 250 Hz. The reason why we chose this database for evaluating our algorithm is that reference annotations have been provided to mark the boundaries of the waveform in addition to those already marked in other databases. More specifically, this database includes annotations for the P and T complexes as wellto the annotations for the Q, R and S complexes, thus helping us to compare the results obtained. Methodology From the discussion on ECG signal morphology in section 2, it can be observed that the points of interest, e.g. P, Q, R, S and T have a distinct and characteristic physical appearance. Furthermore, if any one of these points is known, the rest of the points can be identified from its vicinity with fair accuracy. For example, the P peak is the local maximum between the R peak of the corresponding wave and the T peak of the previous wave; Q minimum is the local minimum between the P peak and the R peak. There are similar neighborhood characteristics for the S and T wave. So by knowing only the position of the R peak, all other waves can be identified from the signal. In this work we exploit these local characteristics of P, Q, R, S and T waves to localize them. The steps followed in this work can be summarized as follows: Step 1: The digitized ECG data from the database are filtered with a bandpass FIR filter with lower and upper cutoff frequency of 3 Hz and 45 Hz respectively to remove noises originating from signals electromyographic (EMG), high-frequency interference, DC offset and baseline wander [8]. Step 2: From the filtered signal, the R peak is extracted using the R segmentation algorithm proposed by Hamilton in [9]. Step 3: After extracting the position of the R peaks, the position of the remaining four peaks is calculated using the local context window in the vicinity of the corresponding R peak. The main contribution of this work lies in step 3 and is discussed in detail in following subsections. After filtering the signal and identifying the R peaks, we move on to identifying the P peaks. As stated previously, the P peak is approximated as the local maximum between the R peak and the T peak of the previous wave. However, when considering the entire region between the T peak and the R peak, there may be an increase in false positives as this region is quite large and can be noisy. and have more peaks and troughs. Therefore, a shortened context window of 100 ms duration is chosen which is shifted from the R peak by 100 ms to the left. A typical boundary of the context window for P-wave detection is marked A and B as shown in Fig. 2. The peak of the P-wave is considered to be the maximum of the values ​​in the context window. Peak T Detection As noted in section 2, peak T possess the unique property of being inverted in some cases. Therefore, within the context window, peak T will be the minimum or maximum, depending on which has the maximum magnitude. To remove this ambiguity, all values ​​within the window are squared. Therefore the T peak will necessarily be in the position of the value having the maximum quadratic magnitude. However, there is a technical problem. If an inverted T peak is present, the voltage level at the peak may be less than 0 V and possibly between 0 mV and -1 mV. In this case, squaring a value between 0 and 1 will, in turn, reduce its magnitude. Therefore a threshold of 1 mV is added to all values ​​before squaring them. T peaks occur quite some time after the QRS wave and may be present in a large region. Therefore the size of the context window is increased to the duration of 200 ms and is shifted to the right 200 ms from the location of the R peak. Fig. 5 shows the boundaries of the A and B window for locating the T peak. Results and discussions In In this section we present a quantitative evaluation of our model. Applying the methods described in Section 4, we annotate all 105 records in the QT database and compare our annotations with those provided in the dataset. The dataset.