Electrocardiography (ecg Or Ekg)

Abstract

Electrocardiography (ECG or EKG) is a widely employed non invasive technique to determine the condition of human heart and detect any abnormal cardiac behavior. Computer systems for ECG analysis can aid physicians in prompt detection of dangerous events such as ventricular fibrillation in patients with high cardiac risks. The first and crucial part of automatic analysis of ECG signals is to accurately identify and measure characteristic features of ECG signal, which is to locate exact position of the onset and offset points of P, QRS and T-waves. In this paper, we propose a fast technique that can accurately identify these key reference points using local windows around R peaks. The proposed method has been tested on standard QT database and a very high accuracy of above 99% is achieved on identifying different segments in ECG signal.

Introduction

An ECG signal originates from the electrical activity of the heart that coordinates the contraction and relaxation of the different chambers of the heart. The analysis of ECG signal and detection of its characteristic points can be used to identify various heart rhythm abnormalities, chest pains and other diseases. One cardiac cycle in a ECG signal comprises P, QRS and T wave complexes. The field of automatic ECG analysis has become quite mature. There has been a lot of prior work done in identifying characteristic points in ECG signals. However most of these use sophisticated and complex signal processing techniques which make them computationally expensive.

In [11], the Pan Tompkins proposed a method which recognizes the QRS complexes using the information on the signal's slope, amplitude and width. But the dual-threshold technique used in this method for searching back the missed out complexes is only useful if the heart rate is regular and unable to find out the missed beats in case of abnormalities. In [12], all the P, QRS and T complexes are detected using wavelet transform method but the P and T onsets and offsets are not detected with much accuracy under serious noise influence. [13] shows detection of the P wave in addition to the QRS complex using 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 the complexes and filtered the high-frequency noise by employing discrete wavelet transform. [16] introduces a new and fast version of ECG delineation algorithm using line fitting but is not robust against certain arrhythmias where no wave is detected. Support vector machine has been used for detection of P and T waves in [17]. In [18] the QRS complexes have been clustered into different groups using self-organizing neural networks for detection. The algorithm proposed in [19] can be evaluated for both clinical and telehealth ECG data. The work in [20] describes a complex QRS detector which is based on dyadic wavelet transform. It gave good performance for multiform premature ventricular contractions, bigeminy, and couplets tapes. [21] employs S-transform to isolate QRS complexes and Shannon energy for localizing R waves. Detection of QRS complexes is also found in [22] that has been performed using difference equation operation. In [23] a QRS complex detector with limited hardware resources has been proposed.

In our paper, we aim towards detecting the P, QRS and T complexes in a reliable and robust way using local windowing which gives a very high detection accuracy and has O(N) computational complexity in detecting P, Q, S and T waves. This paper is organized as follows. In section 2, we present a brief discussion on the anatomy of ECG signal and its characteristic waveforms, Section 3 provides a description of the dataset that has been used to evaluate the proposed method. In section 4, we discuss the methodologies and algorithms implemented in this work. The results that the evaluation has yielded are shown in section 5 and 6 with quantitative as well as qualitative interpretations. Finally, Section 7 concludes the paper.

Review of the Morphology of ECG Signal

The ECG captures the direction and magnitude of electrical depolarization and repolarization generated by a person during his one cycle of heartbeat. The components of a normal ECG tracings consists of multiple waveforms each indicating an electrical event during one heart beat. 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 as a result of its small size [2]. We ignore U wave in this work. P wave marks the beginning of the ECG cycle and is the first short upward movement of the ECG tracing. It indicates that the atria are contracting, pumping blood into the ventricles. It is followed by the QRS complex, normally beginning with a downward deflection, denoted as Q; a larger upward deflection, a peak denoted as R; and then a downward S wave. Fig.1. Schematic diagram of single ECG wave.

The QRS complex represents ventricular depolarization and contraction. The PR interval indicates the transit time for the electrical signal to travel from the sinus node to the ventricles. T wave is normally a modest upwards waveform representing ventricular repolarization. However in certain cases, T wave can be inverted [3]. Each of these wave has a characteristic duration. The P-Wave lasts for about 80 ms. The normal PR interval in an ECG wave ranges from 120 ms to 200 ms. Duration of PR-Segment is 50 ms to 120 ms. The QRS complex duration is about 80 ms to 120 ms. Duration of ST-Segment is 80 ms to 120 ms. Duration of ST-Interval is 320 ms. The QT interval is heart rate dependent. The normal QT intervals are less than 450 ms for men and less than 460 ms for womenbut may vary from 270 ms at a heart rate of 150 beats per min to 500 ms at a heart rate of 40 beats per min [4].

Dataset Description

Several databases are available for studying and analyzing ECG data. The dataset used in this paper is the QT database which contains 105 records, each being 15 minutes in duration [5]. It has been created by incorporating new data from Holter recordings of patients into the MIT-BIH Arrhythmia Database, the European Society of Cardiology ST-T Database and several other databases [6-7].The sampling frequency of all the records in this database is 250 Hz. The reason behind choosing this database for the evaluation of our algorithm is that reference annotations have been given to mark the waveform boundaries in addition to those already marked in the other databases. More specifically, this database includes annotation for P and T complexes in addition to annotations for Q, R and S complexes thus helping us to compare our obtained results.

Methodology

From the discussion on the morphology of ECG signal in section 2, it can be observed that the points of interest viz. P, Q, R, S and T have a distinct and characteristic physical appearance. Also if any one of these points is known, then rest of the points can be identified from its neighborhood with fair accuracy. For instance, P peak is the local maxima between the R peak of the corresponding wave and T peak of the previous wave; Q trough is the local minima between P peak and R peak. Similar neighborhood characteristics exist for S and T wave. Hence by only knowing the position of R peak, all the other waves can be identified from the signal. In this work, we exploit these local features of P, Q, R, S and T waves to locate them.

The steps followed in this work can be summarized as follows:

Step 1: The digitized ECG data from the database is filtered with a bandpass FIR filter with lower and upper cutoff frequency of 3 Hz and 45 Hz respectively to remove noises originating due to electromyogram (EMG) signals, high frequency interferences, DC offset and baseline wandering [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 location of R peaks, the location of remaining four peaks is computed using local context window in the neighborhood of corresponding R peak. The primary contribution of this work is in step 3 and is discussed in detail in following sub-sections. After filtering the signal and locating R peaks, we proceed towards locating P peaks. As stated earlier, P peak is approximated as the local maxima between R peak and T peak of previous wave.

However, considering the entire region between T peak and R peak can lead to increased false positives since this region is quite extended, can be noisy and have multiple peaks and troughs. Hence, a reduced context window of 100 ms duration is chosen which is offset from R peak by 100 ms on the left. A typical boundary of the context window for detecting P waveare marked A and B as shown in Fig 2. The peak of P wave is taken as maximum of the values in the context window.

Detection of T - peak

As stated in section 2, T peak possess a unique property of being inverted in some cases. Thus, within the context window, the T peak will be either the minima or maxima, whichever has the maximum magnitude. To remove this ambiguity, all the values within the window are squared. Thus T peak will necessarily be at the location of the value having maximum squared magnitude. However, there is a glitch. In case there is an inverted T peak, the voltage level at the peak might lie below 0 V, and possibly in between 0 mV and -1 mV. In that case, squaring a value between 0 and 1 will, in turn, reduce its magnitude. Thus a threshold of 1mV is added to all the values before squaring them. T peaks occur fairly long after QRS wave and may be present in an extended region. Thus the size of context window is increased to 200ms duration and is offset to the right by 200 ms from the position of R peak. Fig. 5 shows the window boundaries A and B for locating T peak.

Results and Discussions

In this section we present a quantitative evaluation of our model. By applying the methods described in section 4, we annotate all the 105 records in QT database and compare our annotations with the annotations given in the dataset. The dataset has 9 annotation files in total. To evaluate our proposed method, we chose two of the annotation files from the dataset. The first one is .pu0 annotation which contains automatically determined waveform boundary measurements for all beats. The second set of annotation files considered is .q1c annotation which contains manually determined waveform boundary measurements for a small fraction of beats. We compared our results against reference annotations allowing for a 5% tolerance level; that is, a prediction is deemed correct if its value falls within a range of ±5% of the reference value. As an evaluation metric, for each P, Q, R, S and T, we list the total number of correct predictions, total number of incorrect predictions, overall accuracy and median accuracy across 105 records achieved by our proposed method for respective peaks and troughs.

The results are extremely impressive. We obtain 100% median accuracy on 105 records for all the waves across both the reference annotations. We also obtain very staggering overall accuracy on .pu0 annotations. However the accuracy for .q1c is not as good as that for .pu0. However manual inspection of .q1c annotations showed that some annotations were fairly deviated from where they ought to be. Fig 8 shows an extract of the data of ECG record sel17453 from the dataset and it is annotated from its .q1c file. It can be observed that R and S are not correctly labeled; also T is slightly offset from its appropriate location. We think that small sample size of .q1c annotations accompanied with inaccurate annotations might have affected the statistics that resulted in lower accuracy for P, Q and S. It is also observed from Table 1. that the overall accuracy for T peak detection is lower than its counterparts. It is to be pointed out that detecting T peaks is in fact a non trivial task. Detecting T wave accurately is more challenging than detecting QRS complex due toits low amplitudes, low signal-to-noise ratio (SNR), amplitude and morphology variability, and possible overlapping of the P wave and T wave [10]. The approximation used to detect boundaries of T wave in this proposed method works efficiently for normal ECG signal but may give inaccurate results for certain kinds of abnormal ECG signals. We acknowledge this as a limitation of our proposed method.

Conclusion

In this work, we demonstrated a robust and fast method to detect the P, Q, S and T waves. The algorithm runs in linear time with respect to size of input data because the algorithm is essentially finding maximum or minimum value within an array of numbers. The method is highly accurate, particularly for normal ECG signals. The proposed technique can be used to estimate the boundaries of an ECG signal and then the extracted samples can be used for further analysis.

The authors would like to thank Innovation & Entrepreneurship Development Centre, NIT Silchar for funding this project. The authors are also grateful to Mr. Arkajyoti Saha and Ms. Maitrayee Deb of Silchar Medical College and Hospital for their valuable inputs and suggestions.