Multi-parameter detection of ectopic heart beats

Palaniappan, Rajkumar; Gupta, Cota Navin; Luk, Chan Kap; Krishnan, S.

doi:10.1109/biocas.2004.1454132

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…One work was proposed by Palaniappan et al in 2004, who utilize a set of derivative based features from ECG and arterial blood pressure (ABP) signals, which are then presented to a backpropagation multi layer perceptron (MLP) for classification [ 16 , 17 ]. The not further specified classification performance of 96.47% on the MGH/MF database (Massachusetts General Hospital/Marquette Foundation) seems promising, although the evaluation scheme is not described in depth.…”

Section: Previous Workmentioning

confidence: 99%

A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors

Pflugradt

Geissdoerfer

Goernig

et al. 2017

Sensors

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Automatic detection of ectopic beats has become a thoroughly researched topic, with literature providing manifold proposals typically incorporating morphological analysis of the electrocardiogram (ECG). Although being well understood, its utilization is often neglected, especially in practical monitoring situations like online evaluation of signals acquired in wearable sensors. Continuous blood pressure estimation based on pulse wave velocity considerations is a prominent example, which depends on careful fiducial point extraction and is therefore seriously affected during periods of increased occurring extrasystoles. In the scope of this work, a novel ectopic beat discriminator with low computational complexity has been developed, which takes advantage of multimodal features derived from ECG and pulse wave relating measurements, thereby providing additional information on the underlying cardiac activity. Moreover, the blood pressure estimations’ vulnerability towards ectopic beats is closely examined on records drawn from the Physionet database as well as signals recorded in a small field study conducted in a geriatric facility for the elderly. It turns out that a reliable extrasystole identification is essential to unsupervised blood pressure estimation, having a significant impact on the overall accuracy. The proposed method further convinces by its applicability to battery driven hardware systems with limited processing power and is a favorable choice when access to multimodal signal features is given anyway.

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Section: Previous Workmentioning

confidence: 99%

A Fast Multimodal Ectopic Beat Detection Method Applied for Blood Pressure Estimation Based on Pulse Wave Velocity Measurements in Wearable Sensors

Pflugradt

Geissdoerfer

Goernig

et al. 2017

Sensors

View full text Add to dashboard Cite

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“…A method of identifying PVCs from an SECG signal and flagging them for exclusion from other arrhythmia detection algorithms is desired. Significant research has already been conducted into PVC detection in Holter recordings and other ECG signals [17], [18], [19], [20], [21], [22], but these have rarely been examined in the context of an ICM. The ICM provides additional difficulties due to computational, memory, and power consumption limitations that are not typically present in larger, external recording and analysis systems.…”

Section: Objectives 16mentioning

confidence: 99%

“…Palaniappan et al [19] utilized several morphology features, interbeat interval timing, and blood pressure to classify beats as normal, PVC, or PAC. Their morphology analysis included the R peak amplitude, mobility, and a complexity factor.…”

Section: Dyjach and Carlsonmentioning

confidence: 99%

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Algorithm for Premature Ventricular Contraction Detection from a Subcutaneous Electrocardiogram Signal

Shelly¹

2000

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Cardiac arrhythmias occur when the normal pattern of electrical signals in the heart breaks down. A premature ventricular contraction (PVC) is a common type of arrhythmia that occurs when a heartbeat originates from an ectopic focus within the ventricles rather than from the sinus node in the right atrium. This and other arrhythmias are often diagnosed with the help of an electrocardiogram, or ECG, which records the electrical activity of the heart using electrodes placed on the skin. In an ECG signal, a PVC is characterized by both timing and morphological differences from a normal sinus beat. An implantable cardiac monitor (ICM) is a device used to help physicians diagnose and monitor infrequent cardiac arrhythmias that may not be observed during an ECG recording performed during a normal clinic visit. These devices are implanted under the skin of the chest and simply monitor and record the electrical activity of the heart. The recorded signal is referred to as a subcutaneous electrocardiogram, or SECG. This thesis proposes and tests a novel algorithm that uses an SECG signal to perform PVC detection and is suitable for implementation within an implantable cardiac monitoring device. The proposed algorithm uses a combination of morphological and timing criteria to identify PVCs in near real time. Current commercially-available ICMs do not provide a PVC detection feature, so the proposed algorithm could help provide physicians with valuable additional diagnostic information about a clinically-significant arrhythmia.

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“…For instance, the S-T segment in the ECG signal indicates an imbalance of myocardial oxygen and is used for early diagnosis of ischemia and myocardial infarction, the Q-T interval is a good indicator of long QT syndrome (LQTS) and is also currently the gold standard for evaluating effects of drugs on ventricular repolarisation [2]. Similarly in our previous work [3] we intelligently detected ectopic beats by using information from ECG and blood pressure waveforms. However in many cases the activities relating to given disease are embedded in a single cycle of heart beat.…”

Section: Introductionmentioning

confidence: 99%