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Synchronization of ECG, left ventricular pressure, cardiac sounds and pulse oximetry signals in Göttingen minipigs. Signal synchronization with ECG in black, left ventricle pressure (LVP) in red, cardiac sounds <t>(MIC)</t> in green, and pulse oximetry (POX) in blue. All signals were recorded, at a sampling frequency of 1 kHz, in 1 un-paced healthy swing (panel a ) and 1 un-paced HFrEF swine (panel b ), both with a spontaneous heart rate of circa 80 beats/min. The Microphone Envelope Difference (MED), which is the difference between the upper and lower envelopes of the MIC signals, is displayed in pink. The local minima of the MED sinus-like signal were useful, for each couple of consecutive heart cycles, to approximate the temporal boundary between systole <t>and</t> <t>diastole</t> without using the LVP signal. Note that the cardiac sounds are louder in the un-paced heart failure swine
Mic Signal, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Verlag GmbH mic dta signal
Synchronization of ECG, left ventricular pressure, cardiac sounds and pulse oximetry signals in Göttingen minipigs. Signal synchronization with ECG in black, left ventricle pressure (LVP) in red, cardiac sounds <t>(MIC)</t> in green, and pulse oximetry (POX) in blue. All signals were recorded, at a sampling frequency of 1 kHz, in 1 un-paced healthy swing (panel a ) and 1 un-paced HFrEF swine (panel b ), both with a spontaneous heart rate of circa 80 beats/min. The Microphone Envelope Difference (MED), which is the difference between the upper and lower envelopes of the MIC signals, is displayed in pink. The local minima of the MED sinus-like signal were useful, for each couple of consecutive heart cycles, to approximate the temporal boundary between systole <t>and</t> <t>diastole</t> without using the LVP signal. Note that the cardiac sounds are louder in the un-paced heart failure swine
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Synchronization of ECG, left ventricular pressure, cardiac sounds and pulse oximetry signals in Göttingen minipigs. Signal synchronization with ECG in black, left ventricle pressure (LVP) in red, cardiac sounds (MIC) in green, and pulse oximetry (POX) in blue. All signals were recorded, at a sampling frequency of 1 kHz, in 1 un-paced healthy swing (panel a ) and 1 un-paced HFrEF swine (panel b ), both with a spontaneous heart rate of circa 80 beats/min. The Microphone Envelope Difference (MED), which is the difference between the upper and lower envelopes of the MIC signals, is displayed in pink. The local minima of the MED sinus-like signal were useful, for each couple of consecutive heart cycles, to approximate the temporal boundary between systole and diastole without using the LVP signal. Note that the cardiac sounds are louder in the un-paced heart failure swine

Journal: Journal of Cardiovascular Translational Research

Article Title: Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study

doi: 10.1007/s12265-024-10546-2

Figure Lengend Snippet: Synchronization of ECG, left ventricular pressure, cardiac sounds and pulse oximetry signals in Göttingen minipigs. Signal synchronization with ECG in black, left ventricle pressure (LVP) in red, cardiac sounds (MIC) in green, and pulse oximetry (POX) in blue. All signals were recorded, at a sampling frequency of 1 kHz, in 1 un-paced healthy swing (panel a ) and 1 un-paced HFrEF swine (panel b ), both with a spontaneous heart rate of circa 80 beats/min. The Microphone Envelope Difference (MED), which is the difference between the upper and lower envelopes of the MIC signals, is displayed in pink. The local minima of the MED sinus-like signal were useful, for each couple of consecutive heart cycles, to approximate the temporal boundary between systole and diastole without using the LVP signal. Note that the cardiac sounds are louder in the un-paced heart failure swine

Article Snippet: In addition, for each heartbeat and separately for diastole and systole, we analyzed the melody spectrum (MEL) of the MIC signal, from which we computed two sets of predictors employing the MATLAB® Audio ToolboxTM (The MathWorks, Inc., Natick, MA): i) set A comprising spectral flux [ ], spectral kurtosis [ – ], spectral skewness [ – ], spectral slope [ , ]; ii) set B including the 13 MEL frequency cepstral coefficients in 2nd derivative (delta-delta-MFCCs) [ – ].

Techniques: Sampling