scholarly journals A Robust Motion Artifact Detection Algorithm for Accurate Detection of Heart Rates From Photoplethysmographic Signals Using Time–Frequency Spectral Features

2017 ◽  
Vol 21 (5) ◽  
pp. 1242-1253 ◽  
Author(s):  
Duy Dao ◽  
S. M. A. Salehizadeh ◽  
Yeonsik Noh ◽  
Jo Woon Chong ◽  
Chae Ho Cho ◽  
...  
Keyword(s):  
Motion Artifact ◽  
Detection Algorithm ◽  
Spectral Features ◽  
Artifact Detection ◽  
Heart Rates ◽  
Time Frequency ◽  
Accurate Detection

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

Detection of EEG-Based Eye-Blinks Using A Thresholding Algorithm

2021 ◽  
Vol 6 (4) ◽  
pp. 6-12
Author(s):  
Dang-Khoa Tran ◽  
Thanh-Hai Nguyen ◽  
Thanh-Nghia Nguyen
Keyword(s):  
Detection Algorithm ◽  
Time Frequency ◽  
Eye Blink ◽  
Eye Blinks ◽  
User Training ◽  
Electrical Potentials ◽  
Ocular Artifact ◽  
Blink Detection ◽  
Thresholding Algorithm ◽  
Frequency Properties

In the electroencephalography (EEG) study, eye blinks are a commonly known type of ocular artifact that appears most frequently in any EEG measurement. The artifact can be seen as spiking electrical potentials in which their time-frequency properties are varied across individuals. Their presence can negatively impact various medical or scientific research or be helpful when applying to brain-computer interface applications. Hence, detecting eye-blink signals is beneficial for determining the correlation between the human brain and eye movement in this paper. The paper presents a simple, fast, and automated eye-blink detection algorithm that did not require user training before algorithm execution. EEG signals were smoothed and filtered before eye-blink detection. We conducted experiments with ten volunteers and collected three different eye-blink datasets over three trials using Emotiv EPOC+ headset. The proposed method performed consistently and successfully detected spiking activities of eye blinks with a mean accuracy of over 96%.

An automatic means to discriminate between earthquakes and quarry blasts

1990 ◽  
Vol 80 (6B) ◽  
pp. 2143-2160
Author(s):  
Michael A. H. Hedlin ◽  
J. Bernard Minster ◽  
John A. Orcutt
Keyword(s):  
Fourier Transform ◽  
Spectral Features ◽  
Single Event ◽  
Vertical Array ◽  
Signal To Noise ◽  
Spectral Character ◽  
Frequency Pattern ◽  
Time Frequency ◽  
Cepstral Analysis ◽  
Double Fourier Transform

Abstract In this article we discuss our efforts to use the NORESS array to discriminate between regional earthquakes and ripple-fired quarry blasts (events that involve a number of subexplosions closely grouped in space and time). The method we describe is an extension of the time versus frequency “pattern-based” discriminant proposed by Hedlin et al. (1989b). At the heart of the discriminant is the observation that ripple-fired events tend to give rise to coda dominated by prominent spectral features that are independent of time and periodic in frequency. This spectral character is generally absent from the coda produced by earthquakes and “single-event” explosions. The discriminant originally proposed by Hedlin et al. (1989b) used data collected at 250 sec−1 by single sensors in the 1987 NRDC network in Kazakhstan, U.S.S.R. We have found that despite the relatively low digitization rate provide by the NORESS array (40 sec−1) we have had good success in our efforts to discriminate between earthquakes and quarry blasts by stacking all vertical array channels to improve signal-to-noise ratios. We describe our efforts to automate the method, so that visual pattern recognition is not required, and to make it less susceptible to spurious time-independent spectral features not originating at the source. In essence, we compute a Fourier transform of the time-frequency matrix and examine the power levels representing energy that is periodic in frequency and independent of time. Since a double Fourier transform is involved, our method can be considered as an extension of “cepstral” analysis (Tribolet, 1979). We have found, however, that our approach is superior since it is cognizant of the time independence of the spectral features of interest. We use earthquakes to define what cepstral power is to be expected in the absence of ripple firing and search for events that violate this limit. The assessment of the likelihood that ripple firing occurred at the source is made automatically by the computer and is based on the extent to which the limit is violated.

scholarly journals Minimally-modeled search of higher multipole gravitational-wave radiation in compact binary coalescences.

2021 ◽  
Author(s):  
G. Vedovato ◽  
Edoardo Milotti ◽  
Giovanni Andrea Prodi ◽  
Sophie Bini ◽  
Marco Drago ◽  
...  
Keyword(s):  
Wave Radiation ◽  
Full Detail ◽  
Parameter Estimates ◽  
Spectral Features ◽  
Operating Characteristics ◽  
Fast Analysis ◽  
Time Frequency ◽  
Compact Binary ◽  
Frequency Representation ◽  
Novel Method

Abstract As the Advanced LIGO and Advanced Virgo interferometers, soon to be joined by the KAGRA interferometer, increase their sensitivity, they detect an ever-larger number of gravitational waves with a significant presence of higher multipoles in addition to the dominant (2, 2) multipole. These higher multipoles can be detected with different approaches, such as the minimally-modeled burst search methods, and here we discuss one such approach based on the coherent WaveBurst pipeline (cWB). During the inspiral phase the higher multipoles produce chirps whose instantaneous frequency is a multiple of the dominant (2, 2) multipole, and here we describe how cWB can be used to detect these spectral features. The search is performed within suitable regions of the time-frequency representation; their shape is determined by optimizing the Receiver Operating Characteristics. This novel method has already been used in the GW190814 discovery paper (Astrophys. J. Lett. 896 L44) and is very fast and flexible. Here we describe in full detail the procedure used to detect the (3, 3) multipole in GW190814 as well as searches for other higher multipoles during the inspiral phase, and apply it to another event that displays higher multipoles, GW190412, replicating the results obtained with different methods. The procedure described here can be used for the fast analysis of higher multipoles and to support the findings obtained with the model-based Bayesian parameter estimates.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

Volcanic Tremor Extraction and Earthquake Detection Using Music Information Retrieval Algorithms

2021 ◽  
Author(s):  
Zahra Zali ◽  
Matthias Ohrnberger ◽  
Frank Scherbaum ◽  
Fabrice Cotton ◽  
Eva P. S. Eibl
Keyword(s):  
Amplitude Spectrum ◽  
Volcanic Tremor ◽  
Volcanic Eruptions ◽  
Detection Algorithm ◽  
Transient Signal ◽  
Volcanic Origin ◽  
Time Frequency ◽  
Reconstructed Signal ◽  
Single Station ◽  
Transient Events

Abstract Volcanic tremor signals are usually observed before or during volcanic eruptions and must be monitored to evaluate the volcanic activity. A challenge in studying seismic signals of volcanic origin is the coexistence of transient signal swarms and long-lasting volcanic tremor signals. Separating transient events from volcanic tremors can, therefore, contribute to improving upon our understanding of the underlying physical processes. Exploiting the idea of harmonic–percussive separation in musical signal processing, we develop a method to extract the harmonic volcanic tremor signals and to detect transient events from seismic recordings. Based on the similarity properties of spectrogram frames in the time–frequency domain, we decompose the signal into two separate spectrograms representing repeating (harmonic) and nonrepeating (transient) patterns, which correspond to volcanic tremor signals and earthquake signals, respectively. We reconstruct the harmonic tremor signal in the time domain from the complex spectrogram of the repeating pattern by only considering the phase components for the frequency range in which the tremor amplitude spectrum is significantly contributing to the energy of the signal. The reconstructed signal is, therefore, clean tremor signal without transient events. Furthermore, we derive a characteristic function suitable for the detection of transient events (e.g., earthquakes) by integrating amplitudes of the nonrepeating spectrogram over frequency at each time frame. Considering transient events like earthquakes, 78% of the events are detected for signal-to-noise ratio = 0.1 in our semisynthetic tests. In addition, we compared the number of detected earthquakes using our method for one month of continuous data recorded during the Holuhraun 2014–2015 eruption in Iceland with the bulletin presented in Ágústsdóttir et al. (2019). Our single station event detection algorithm identified 84% of the bulletin events. Moreover, we detected a total of 12,619 events, which is more than twice the number of the bulletin events.

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