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Signal analysis method

A technique for signals and execution methods, applied in the field of signal analysis, to solve problems affecting model performance and reliability

Inactive Publication Date: 2007-06-06
ISIS INNOVATION LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Such "double stroke" segmentation seriously affects the performance and reliability of the model

Method used

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  • Signal analysis method
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Embodiment Construction

[0052] The following specific embodiments of the invention are based on Hidden Markov Models (HMMs) that are trained to recognize one or more of the ECG signal characteristic curves. The model consists of states, each state representing a specific region of an ECG signal. A graphical depiction of the structure or "topology" of a class of Hidden Markov Models for ECG segmentation is shown in Figure 7a. The model consists of six single-valued states, which in turn represent the P wave, the baseline portion between the end of the P wave and the onset of the QRS complex (defined as "Baseline 1"), the QRS complex, the T wave, the U wave, and the T wave. The baseline portion between the end of the wave (or U wave, if there is one) and the beginning of the P wave of the next beat. These portions of the ECG waveform are shown in FIG. 1 . A number of alternative Hidden Markov Model structure types for ECG segmentation are illustrated in Figures 7b-7e. In each case, the model consist...

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Abstract

Improvement in the reliability of segmentation of a signal, such as an ECG signal, is disclosed through the use of duration constraints. The signal is analysed using a hidden Markov model. The duration constraints specify minimum allowed durations for specific states of the model. The duration constraints can be incorporated either in the model itself or in a Viterbi algorithm used to compute the most probable state sequence given a conventional model. Also disclosed is the derivation of a confidence measure from the model which can be used to assess the quality and robustness of the segmentation and to identify any signals for which the segmentation is unreliable, for example due to the presence of noise or abnormality in the signal.

Description

technical field [0001] The invention relates to a method for signal analysis. By utilizing hidden Markov modeling techniques combined with duration constraints, the analysis can include automatic segmentation of signals, such as electrocardiograms (ECG) or other biomedical signals, into component waveform signatures. Optionally, it can also be derived in conjunction with an associated confidence measure representing segmentation accuracy and robustness. Background technique [0002] The ECG, also known by the acronym EKG, is an important non-invasive signal for measuring the electrical power of the heart. Each individual heartbeat is made up of many different cardiological phases which in turn give rise to different characteristic curves in a set of ECG waveforms. These characteristic curves represent the depolarization (discharging) or repolarization (charging) of muscle cells in specific areas of the heart. Figure 1 shows a human ECG waveform and associated characterist...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/0452
CPCA61B5/0452A61B5/7221A61B5/726A61B5/7264A61B5/7267G16H50/20A61B5/349
Inventor 尼古拉斯·休斯莱昂内尔·塔拉塞恩科斯蒂芬·罗伯茨
Owner ISIS INNOVATION LTD