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Chaos Theory: An Emerging Tool for Arrhythmia Detection

机译：Chaos理论：一种用于心律失常检测的新兴工具

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摘要

The heart is an important muscular organ of the human body which pumps blood throughout the body. It is essential for human life. Timely and accurate assessment of the functioning of the heart has great relevance for reducing the death rate due to cardiac diseases around the world. If the heart is not able to pump blood smoothly, then heart diseases are likely to appear. These heart diseases are known as arrhythmia. Electrocardiogram (ECG) is a diagnostic tool for assessing the functioning of heart non-invasively. It not only detects cardiovascular diseases, but also examines breathing pattern and mental stress. ECG appears in the form of an electrical signal that comprises of P-QRS-T waves and is captured by pasting electrodes on the surface of the skin in a conductive medium. Features of these wave components, such as clinical frequencies, heart rate (HR) measurement, RR interval measurement, spectral components, non-linearity, trajectory identification, and amplitudes, help doctors to diagnose cardiac arrhythmias accurately. This paper presents a computer aided diagnosis (CAD) system to extract non-linearity and trajectory patterns using the theory of chaos analysis to aid cardiologists diagnose arrhythmia accurately. ECG signal is non-stationary and non-linear in nature due to which it contains multiple time-varying frequencies. So a more reliable and accurate technique like time– frequency transform such as short-time Fourier transform (STFT) etc. is needed. In this paper, STFT is used, which is an efficient technique to observe frequency contents of small non-linear segments in time domain. It is used to determine the sinusoidal frequency and phase content of the local sections of a signal. R-peak is very crucial for classifying cardiac arrhythmia. Therefore, STFT is used for detecting R-peaks and their frequency contents. Due to limited time–frequency resolution, STFT usually misses on some information. Therefore, there is a need of supplementing the existing research on the ECG signal interpretation by using non-linear techniques. These gaps have motivated us to use chaos theory (analysis) for ECG signal analysis. The non-linear techniques are expected to yield supplementary clues about the non-linearities in the considered segment. In chaos analysis, the sketched trajectories represent the flow of the system where each trajectory involves a subregion of the phase space known as an attractor. In phase space, set of points depicts complete status of the cardiac cycles through which the system migrates over time. An attractor showcases the best preview according to the initial conditions and time delay dimension. The shape of an attractor may be oval, egg-shaped, circular and in some cases corn-type. It increases the decision capability of the proposed system by identifying correct arrhythmia type. For validating this research work, physioNet database [Massachusetts Institute of Technology-Beth Israel Hospital Arrhythmia database (M-BArr DB), Ventricular Tachyarrhythmia database (VT DB)] and real time database (R T DB) have been used. The proposed technique has been evaluated on the basis of sensitivity (Se) and positive predictive value (PPV). Se of 99.92% and PPV of 99.93% are obtained for the considered databases (M-BArr DB, VT DB and RT DB). Chaos theory together with STFT has proved itself as a good approach that reduces the occurrence of spurious outcomes and has demonstrated the properties that are typical outlook of deterministic chaotic systems.
机译：心脏是人体的重要肌肉器官，泵体整个身体。这对人类生命至关重要。及时准确地评估心脏功能的功能与世界各地心脏病导致的死亡率有很大的相关性。如果心脏无法顺畅地泵送血液，则可能出现心脏病。这些心脏病被称为心律失常。心电图（ECG）是一种用于评估心脏非侵入性功能的诊断工具。它不仅检测心血管疾病，还要检查呼吸模式和精神压力。 ECG以电信号的形式出现，所述电信号包括P-QRS-T波，并且通过粘贴在导电介质的皮肤表面上的电极捕获。这些波部件的特征，如临床频率，心率（HR）测量，RR间隔测量，光谱分量，非线性，轨迹识别和幅度，帮助医生准确诊断心脏心律失常。本文提出了一种计算机辅助诊断（CAD）系统，用于利用混沌分析理论提取非线性和轨迹模式，以帮助心脏病学家准确诊断心律失常。由于它包含多个时变频率，ECG信号是非静止和非线性的。因此，需要一种更可靠和准确的技术，如时频变换，例如短时傅里叶变换（STFT）等。在本文中，使用STFT，这是一种高效的技术，可以在时域中观察小非线性段的频率内容。它用于确定信号的局部部分的正弦频率和相位含量。 R峰对于对心律失常进行分类非常重要。因此，STFT用于检测R峰值及其频率内容。由于时频分辨率有限，Stft通常会错过一些信息。因此，需要通过使用非线性技术来补充关于ECG信号解释的现有研究。这些差距动机使我们使用混沌理论（分析）进行ECG信号分析。预计非线性技术将产生关于所考虑的段中的非线性的补充线索。在混沌分析中，速写轨迹代表每个轨迹的系统的流程，其中每个轨迹涉及称为吸引子的相位空间的子区域。在相位空间中，一组点描绘了系统通过时间迁移的心脏周期的完整状态。吸引子根据初始条件和时间延迟维度展示最佳预览。吸引物的形状可以是椭圆形，蛋形，圆形和一些情况的玉米型。它通过识别正确的心律失常类型来增加所提出的系统的决策能力。为了验证这项研究工作，PhysioIonet数据库[Massachusetts Technology Technology-Beth以色列医院的心律失常数据库（M-BART DB），心室性心律失常数据库（VT DB）和实时数据库（R T DB）已经使用。已经基于灵敏度（SE）和阳性预测值（PPV）评估所提出的技术。对于所考虑的数据库（M-BART DB，VT DB和RT DB），获得99.92％和PPV的PPV为99.92％。 Chaos理论与Stft一起证明了自己作为一种良好的方法，这减少了虚假成果的发生，并证明了确定性混沌系统的典型前景的特性。

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