A biomedical signal segmentation algorithm for event detection based on slope tracing

Kim, Jungkuk; Kim, Minkyu; Won, Injae; Yang, Seungyhul; Lee, Ki-Young; Huh, Woo Seong

doi:10.1109/iembs.2009.5333874

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Some improvements have been noted using the double-threshold method [9,10] to overcome these shortcomings. Moreover, by using signal properties such as the amplitude, slope, deflection width, or distance between neighboring deflections, Kim et al [11] proposed using a slope-tracing-based algorithm to separate the intervals of the carrier signals. However, these thresholds methods have to face the critical issue of discovering the proper threshold values.…”

Section: Introductionmentioning

confidence: 99%

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FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

Huang

Wang

2022

Electronics

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Carrier signal detection is a complicated and essential task in many domains because it demands a quick response to the existence of several carriers in the wideband, while also precisely predicting each carrier signal’s frequency centers and bandwidths, including single-carrier and multi-carrier modulation signals. Multi-carrier modulation signals, such as FSK and OFDM, could be incorrectly recognized as several single-carrier signals by using the spectrum center net (SCN) or FCN-based method. This paper designed a deep convolutional neural network (CNN) framework for multi-carrier signal detection by fusing the features of multiple consecutive frames of the broadband power spectra and estimating the information of each single-carrier or multi-carrier modulation signal in the broadband, called frame fusion spectrum center net (FFSCN), including FFSCN-R, FFSCN-MN, and FFSCN-FMN. FFSCN includes three base parts, the deep CNN-based backbone, the feature pyramid network (FPN) neck, and the regression network (RegNet) head. FFSCN-R and FFSCN-MN fusing the FPN out features, which use the Residual and MobileNetV3 backbone, respectively, and FFSCN-MN cost less inference time. To further reduce the complexity of FFSCN-MN, the designed FFSCN-FMN modifies the MobileNet blocks and fuses the features at each block of the backbone. The multiple consecutive frames of broadband power spectra not only preserve the high-resolution ratio of the broadband frequency, but also add the features of the signal changes in the time dimension. Extensive experimental results demonstrate that the proposed FFSCN can effectively detect multi-carrier and single-carrier modulation signals in the broadband power spectrum and outperform SCN in accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

“…Recently, some studies [12][13][14] found that using deep learning for carrier signal detection achieves more robust and higher performance than threshold-based methods [9][10][11]. These deep-learning-based methods apply a broadband power spectrum as the input.…”

Section: Introductionmentioning

confidence: 99%

FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

Huang

Wang

2022

Electronics

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“…This evolution not only included the macro-analysis of gross processes but also the detection and analysis of microevents within each gross process [3]. As mentioned before, biomedical signals carry the signatures of many processes and artifacts, which makes the extraction/identification of the specific part of interest (called event or epoch), the first step of any systematic signal analysis or monitoring [4]. Further, the need for robust event extraction algorithms for biomedical signals is driven by the exponential growth of the amount and complexity of data generated by biomedical systems [5].…”

Section: Introductionmentioning

confidence: 99%

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

2021

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Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.

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An ECG signal processing algorithm based on removal of wave deflections in time domain

Kim

Won

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This paper introduces a new approach to process biomedical signals by surgically removing wave deflections in time domain. The method first determines the epochs of high frequency deflections, cuts out them from the signal, and then connects the two disconnected points. To determine the epoch of a deflection to be removed, four slope trace waves are used to isolate the deflection based on signal characteristics of amplitude, slope, duration, and distance from neighboring deflections. The method has been applied to simulated data and MIT-BIH arrhythmia database to show its practical efficacy in the case of baseline wandering removal. It is found that the method has the capability to identify and remove high frequency deflections appropriately, leaving low frequency deflection such as baseline drifting.

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A biomedical signal segmentation algorithm for event detection based on slope tracing

Cited by 6 publications

References 6 publications

FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

FFSCN: Frame Fusion Spectrum Center Net for Carrier Signal Detection

A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

An ECG signal processing algorithm based on removal of wave deflections in time domain

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