Video Lectures on "Bioelectrical Signal Processing in Cardiac and Neurological Applications" by Prof. Leif Sornmo

Lecture Videos:

Leif Sörnmo: Bioelectrical Signal Processing in Cardiac and Neurological Applications

Topics to be Covered :
1. Introduction  
3. The Brain  
11. Evoked Potentials 
24. The Electrical Activity of the Heart

1	Introduction Bioelectrical Signal Processing The Human Body The Biomedical Signal: Reflections of a Secret Origin of Bioelectrical Signals Why this Textbook? Where is Bioelectricity Measured? The Brain, Heart and Muscles and their Electrical Activity Electrical Signals - Spontaneous Electrical Signals - Stimulation Multimodal Signal Recording
2	Purposes of Biomedical Signal Processing Signal Processing Constrains Noisy Signal Situation - Stress Testing
3	The Biomedical Signal Processing Challenge Why Simulated Signals? The Brain Neurons = Nerve Cells Inside the Neuron Cerebral Cortex and its Lobes The Cerebral Cortex: Some Basic Facts The Nervous System Electroencephalogram - EEG EEG - (Un)synchronized Activity EEG acquisition Important EEG Rhythms EEG Signals - Examples Alpha, Beta and Blink Artifacts The Use of EEG Today Onset of Epileptic Seizure
4	Brain Computer Interface BCI Principle Brain Computer Interface BrainGate EEG Modeling Aspects Noise and Artifacts EMG in the EEG Eye Movements and the EEG "Optimal" Noise Rejection "Eye Movement" Electrodes Noise Rejection by Weighting
5	Linear Weighting Weights and the MSE MSE Minimization Assumption of Stationarity Noise Rejection by Weighting Assumption of Stationarity Correction of ElectroOculoGram Adaptive Noise Rejection MSE Criterion Minimization Noise Rejection - Filtered Reference Signals
6	Spectral Analysis of the EEG Fourier-Based Spectral Analysis The Periodogram Properties of the Periodogram Periodogram and Segmentation Spectral Parameters Trending of Spectral Parameters EEG and AR Modeling
7	Autoregressive (AR) Modeling AR Modeling and Linear Prediction AR Parameter Estimation The Normal Equation Multivariate AR Models EEG Activity Propagation after Finger Movement AR Modeling and Sampling Rate
8	AR Modeling and Sampling Rate Adaptive EEG Segmentation Segmentation - Sliding Window Criterion for EEG Segmentation Spectra of a Segmented Signal Spectral Analysis of Nonstationary Signals Criterion for EEG Segmentation Spectral Analysis of Nonstationary Signals Short-Time Fourier Transformation Photic Stimulation at Different Rates Heart Surgery of an Infant
9	STFT and Beyond Time-Varying AR Model Time-Varying AR during Seizure WWD-based Time-Frequency Analysis The Ambiquity Function Ambiquity Function, cont't The Analytical Signal Analytical Signal in Math Terms
10	Ambiguity Function, cont' Wigner-Ville Distribution (WVD) Comparison of STFT and WVD The Pseudo WVD Cross-Term Reduction CWD and 2-Component Signal
11	Evoked Potentials EP = A Transient Waveform Examples of Evoked Potentials EP - Definitions Auditive Evoked Potentials - AEPs Visual Evoked Potentials - VEPs Somatosensory Evoked Potentials - SEPs SEPs during Spinal Surgery EP Scalp Distribution Brainstem Auditive EP in Newborns BAEPs of Healthy Children
12	Cognitive EPs Ensemble Formation Formation of an EP Ensemble 10 Superimposed EPs Model for Ensemble Averaging Noise Assumptions
13	Ensemble Averaging Noise Variance Noise Assumptions Reduction of Noise Level Exponential Averaging
14	Introduction Noise Reduction of EPs with Varying Noise Level Weighted Averaging
15	Weighted Averaging, cont' Weighted Averaging: An Example Robust Waveform Averaging The Effect of Latency Variations Lowpass Filtering of the Signal Latency Variation and Lowpass Filtering Techniques for Correction of Latency Variations Estimation of Latency Woody's Method Woody's Method: Different SNRs Noise Reduction by Filtering Wiener Eiltering Filtering of Evoked Potentials Limitations of Wiener Filtering
16	Problem Solving
17	Problem Solving
18	Tracking of EP Morphology Selection of Basis Functions Orthogonal Expansions Basis Functions: An Example Calculation of the Weights Mean-Square Weight Estimation
19	Truncated Expansion Examples of Basis Functions Sine/Cosine Modeling MSE Basis Functions Karhunen-Loeve Basis Functions KL Performance Index
20	Karhunen-Loeve Basis Functions How to get Rx? Example: KL Basis Functions Time-Varying Filter Interpretation Modeling with Damped Sinusoids Adaptive Estimation of Weights Estimation Using Sine/Cosine Estimation Using KL Functions Limitations
21	Wavelet Analysis Wavelet Applications The Correlation Operation the Mother Wavelet The Wavelet Transform The Scalogram The Discrete Wavelet Transform Multiresolution Analysis
22	Multiresolution Analysis, cont' Multiresolution Analysis Exemplified The Scaling Function The Approximation Signal x0(t) Multiresolution Analysis The Approximation Signal xj(t) The Multiresolution Property
23	The Wavelet Function The Wavelet Series Expansion Multiresolution Signal Analysis: A Classical Example The Refinement Equation The Haar Scaling Function Maar Multiresolution Analysis Haar Scaling and Wavelet Functions Computational Coefficients Filter Bank Implementation DWT Calculation Inverse DWT Calculation Coifflet Multiresolution Analysis Scaling Coefficient in Noise The Wavelet Series Expansion Denoising of Evoked Potentials EP Wavelet Analysis
24	The Electrical Activity of the Heart The Heart ... Blood Flow od the Heart Conduction System of the Heart Cardiac Excitation Electrical Vectors of the Heart Cardiac Excitation The Cardiac Cycle & Wave Shape Extremity Leads - I, II, III Årecordial Leads - V1 to V6 The Standard 12-Lead ECG
25	ECG Waves: P-QRS-T Normal Sinus Rhythms Heart Rate Variability Arrhythmias: Ectopic Beats Arrhythmias: Bi & Trigenimy Arrhythmias: Atrial Flutter/Fibrillation Arrhythmias: Ventricular Flutter/Fibrillation
26	Heart Attack Myocardial Ischemia Noise in the ECG Clinical ECG Applications The Exercise Stress Test Stress Testing and Ischemia - ECG Reaction 1 Stress Testing and Ischemia - ECG Reaction 2 ST Reaction Versus Heart Rate - Decision Regions High-Resolution ECG and Cardiac Late Potentials Spectral Analysis of the ECG? Spectral Analysis of the Heart Rate? EEG, EP, and ECG: Time Base? ECG Signal Processing ECG Filtering Techniques ... ECG Baseline Wander
27	Discussion
28	Baseline Filtering: Phase Aspects Baseline Filtering: An Example Cubic Spline Interpolation 50/60 Hz LTI Notch Filter Nonlinear 50-Hz Filtering Nonlinear Filtering Exemplified 50/60 Hz Filtering
29	50/60 Hz Filtering QRS Detection Problems Spectral Detection Problems QRS Detection Models for QRS Detection Design of Linear Detection Filter Simple Detector Filter Structures Simple Filters for QRS Detection - Frequency Response Design of Nonlinear Transformation Envelope-based Detection Envelope Examples Preprocessor Output QRS Detection: Decision Rule QRS Detector Performance
30	QRS End Delineation LPD-based Delineation Data Compression ECG Data Redundancy Data Compression of ECG Signals Lossless Data Compression based on Linear Prediction Linear Prediction Lossy Data Compression Example of AZTEC The SAPA Principle Example of SAPA KLT-based Data Compression KLT Compression with Tolerance KLT using Universal Data KLT using Subject-Specific Data Handling Interbeat Redundancy Performance Measures
31	What's behind the Beat? Heart Rate Variability (HRV) Conductor's Heart Rate Heart Rate and Respiration RR Interval Series 24-hour RR Interval 24h RR Interval Histograms The RR Interval Series - the Tachogram Heart Rhythm Representations Lowpass Filtered Event Series
32	Integral Pulse Frequency Modulation (IPFM) Model Output of the IPFM Model Heart Timing Signal Heart Timing Signal: Example Why Spectral Analysis of HRV? Spectrum of Counts Lomb's Periodogram Lomb's Periodogram and the Classical Periodogram HRV Spectral Analysis HRV Spectrum: A Comparison Power Spectrum with One Modulation Frequency (0.16 Hz) Power Spectrum with Two Modulation Frequencies HRV and Sudden Cardiac Death
33	Ectopic Beat Correction Do not maltreat the signals ...