ECG Signal Processing and Waveform Recognition

The P-wave represents the electrical activity of the atria as they depolarize, causing a positive deflection on the left leg electrode.

The PR-segment indicates a delay in the electrical signal as it passes through the AV-node, resulting in no electrical activity.

The PR-interval encompasses both the P-wave and PR-segment, reflecting the time it takes for the electrical signal to travel from the SA-node to the ventricles.

The Q-wave represents the initial depolarization of the interventricular septum, producing a negative deflection on the left leg electrode due to the larger muscle mass on the left side.

The R-wave corresponds to the rapid depolarization of both ventricles, creating a large positive deflection on the left leg electrode because of the dominant left ventricle.

The S-wave represents the depolarization spreading upwards through the ventricles, creating a negative deflection on the left leg electrode after the R-wave

V1 and V2 are located in the fourth intercostal space. V3 is positioned halfway between V2 and V4. V4, V5, and V6 are situated on a horizontal line, not necessarily following intercostal spaces.

The QRS complex represents the overall electrical activity of the ventricles, encompassing the Q-wave, the R-wave, and the S-wave. It signifies the ventricular depolarization.

The ST-segment is a flat line on the ECG, indicating a brief pause between ventricular depolarization and repolarization, ensuring the ventricles are fully charged before repolarizing.

The T-wave represents the repolarization of the ventricles, where electrical charges return to their resting state. It's characterized by a positive deflection on the left leg electrode because the movement of negative charges away from the positive electrode.

The Pan-Tomkins algorithm is a widely used method for identifying the QRS complex in ECG signals. It involves a series of steps to enhance the QRS complex, including filtering, differentiation, squaring, integration, and thresholding, followed by peak detection.

Digital filtering based techniques are similar to the Pan-Tomkins algorithm, but they utilize more advanced filtering methods and transform-based feature extraction techniques to detect the QRS complex.

Base-line drift during ECG recording can cause the baseline to shift upwards or downwards, which can affect the analysis of the ECG signal. This drift can be caused by temperature variations, electrode placement, or other factors.

Precordial leads, also known as chest leads, provide a closer view of the electrical activity in the heart, allowing for more detailed analysis of specific areas. These leads are placed at different locations on the chest.

ECG recordings can be affected by noise from the recording system, such as electrical interference. This noise can be minimized by using proper grounding techniques and shielding.

Motion artifacts can occur during ECG recording due to patient movements, such as coughing or breathing, causing distortions in the signal. Limblead ECG is particularly susceptible to motion artifacts.

A heart arrhythmia is a condition where the heartbeat is irregular. Some types are harmless, but others can be dangerous

The process of detecting the QRS complex in an ECG signal involves analyzing the signal's electrical activity to locate the heart's ventricular depolarization.

A measurement of the time between two consecutive R peaks in an ECG signal, reflecting the duration of one heartbeat cycle.

This refers to the slope of the ECG signal between the end of the QRS complex and the beginning of the T wave, providing insight into the recovery phase of the heart's ventricles.

The P-wave is generated by the electrical activity of the atria as they contract, indicating the spread of an electrical impulse through the upper chambers of the heart.

A rapid heart rate, above the normal range for a person's age and condition. Usually a rate of over 100 bpm

A slow heart rate, below the normal range for a person's age and condition. Usually a rate of under 60 bpm

The QRS complex represents the electrical activity associated with the ventricles contracting, as the signal passes through the ventricular muscle. Important for diagnosing heart conditions.

Changes in signal characteristics (e.g., frequency, amplitude) can cause disturbances in ECG signals, leading to inaccurate readings.

Adaptive filters automatically adjust their settings to improve signal quality based on the current noise level.

Adaptive filters are particularly good at dealing with noise that changes over time, which is common in ECG recordings.

Electromagnetic interference from electrical devices can create a humming noise in ECG signals.

Slow, gradual shifts in the ECG signal, often caused by breathing or patient movement.

Electrical signals from muscle activity can be picked up by ECG electrodes, leading to jagged noise.

Sudden, unpredictable changes in the ECG signal caused by electrode movement or patient motion.

Adaptive filters can be used to remove a variety of noise from ECG signals, improving the accuracy of heart rhythm analysis.

Evoked responses (ERs) are brain's time-locked electrical responses to specific sensory, cognitive, or motor stimuli.

Sensory evoked potentials (SEPs) are generated by sensory stimuli (e.g., visual, auditory, or somatosensory). Examples include Visual Evoked Potentials (VEPs), Auditory Evoked Potentials (AEPs), and Somatosensory Evoked Potentials (SSEPs).

Cognitive evoked potentials reflect higher-level brain functions. Examples include P300 (decision-making) and N400 (language processing).

Averaging techniques are used to enhance the signal-to-noise ratio (SNR) of evoked responses, which are often obscured by background EEG activity and noise.

Time-locked averaging involves recording ERs across multiple trials of the same stimulus, aligning them by the stimulus onset, and averaging them to preserve the consistent signal (ER) and cancel out random noise.

Weighted averaging assigns weights to trials based on their quality, such as giving lower weight to noisy trials, to improve robustness against outliers.

Artifact rejection involves excluding trials with excessive noise (e.g., muscle artifacts or electrode movement) before averaging to further enhance the signal quality.

Pattern recognition techniques analyze EEG data to identify brain activity patterns. These techniques include time-domain analysis, frequency-domain analysis, automated signal processing, and visualization.

An electrophysiological technique used to measure the electrical activity of muscles during contraction and relaxation.

Electrodes placed on the skin to measure muscle activity non-invasively.

Electrodes inserted into the muscle to provide detailed measurements.

Amplifying weak EMG signals to make them detectable.

Removing unwanted signals like noise and movement from EMG data.

Analyzing EMG signals by looking at their changes over time, useful for detecting muscle activity patterns.

Using EMG signals to control external devices like prosthetic limbs.

This type of biofeedback employs real-time brain activity monitoring to train users to regulate their brainwaves.