Skip to main content

Positive Occipital Sharp Transients of Sleep compared to Interictal Epileptiform Discharges

Positive Occipital Sharp Transients of Sleep (POSTS) and interictal epileptiform discharges (IEDs) are both EEG patterns, but they have distinct characteristics, clinical implications, and contexts. 

Positive Occipital Sharp Transients of Sleep (POSTS)

1.      Definition:

§  POSTS are sharp waveforms that occur predominantly during sleep, particularly in non-rapid eye movement (NREM) sleep.

2.     Waveform Characteristics:

§  They typically exhibit a triangular shape and can be monophasic or diphasic. The first phase usually has a higher amplitude than the second phase.

3.     Location:

§  Recorded primarily from the occipital leads (O1 and O2) of the EEG, with a positive field at the occiput. Phase reversals are often observed at these electrodes.

4.    Duration and Frequency:

§  Each transient lasts approximately 80 to 200 milliseconds and can occur in trains, typically lasting about 1 to 2 seconds.

5.     Clinical Significance:

§  Generally considered a normal variant in healthy individuals, especially in children and adolescents. They are not associated with any pathological conditions and are common in the EEGs of healthy adults.

6.    Age-Related Variability:

§  More prevalent in younger populations and tend to decrease with age. Rarely observed in individuals over 70 years old.

Interictal Epileptiform Discharges (IEDs)

7.     Definition:

§  IEDs are abnormal EEG patterns that occur between seizures in individuals with epilepsy. They represent a transient abnormality in the brain's electrical activity.

8.    Waveform Characteristics:

§  IEDs can vary in morphology but are often characterized by sharp waves or spikes. They typically have a more asymmetric shape compared to POSTS and may show a sharper contour.

9.    Location:

§  IEDs can occur in various regions of the brain, depending on the type of epilepsy. They are not limited to the occipital region and can be localized to specific areas associated with seizure activity.

10.                        Duration and Frequency:

§  IEDs are usually brief, lasting less than 100 milliseconds, and can occur sporadically or in bursts. They do not typically occur in trains like POSTS.

11.  Clinical Significance:

§  The presence of IEDs is indicative of an underlying epileptic condition and may correlate with seizure activity. They are considered abnormal findings and can help in diagnosing epilepsy.

12. Age-Related Variability:

§  IEDs can occur in individuals of any age with epilepsy, but their presence and frequency may vary based on the type of epilepsy and the individual's age.

Summary

In summary, while both Positive Occipital Sharp Transients of Sleep and interictal epileptiform discharges are observed in EEG recordings, they differ significantly in their characteristics, clinical implications, and contexts. POSTS are generally benign and associated with normal sleep activity, while IEDs are abnormal findings indicative of epilepsy and potential seizure activity. The identification of POSTS suggests normal sleep function, whereas the presence of IEDs raises concerns about underlying neurological conditions.

 

Comments

Popular posts from this blog

Relation of Model Complexity to Dataset Size

Core Concept The relationship between model complexity and dataset size is fundamental in supervised learning, affecting how well a model can learn and generalize. Model complexity refers to the capacity or flexibility of the model to fit a wide variety of functions. Dataset size refers to the number and diversity of training samples available for learning. Key Points 1. Larger Datasets Allow for More Complex Models When your dataset contains more varied data points , you can afford to use more complex models without overfitting. More data points mean more information and variety, enabling the model to learn detailed patterns without fitting noise. Quote from the book: "Relation of Model Complexity to Dataset Size. It’s important to note that model complexity is intimately tied to the variation of inputs contained in your training dataset: the larger variety of data points your dataset contains, the more complex a model you can use without overfitting....

Linear Models

1. What are Linear Models? Linear models are a class of models that make predictions using a linear function of the input features. The prediction is computed as a weighted sum of the input features plus a bias term. They have been extensively studied over more than a century and remain widely used due to their simplicity, interpretability, and effectiveness in many scenarios. 2. Mathematical Formulation For regression , the general form of a linear model's prediction is: y^ ​ = w0 ​ x0 ​ + w1 ​ x1 ​ + … + wp ​ xp ​ + b where; y^ ​ is the predicted output, xi ​ is the i-th input feature, wi ​ is the learned weight coefficient for feature xi ​ , b is the intercept (bias term), p is the number of features. In vector form: y^ ​ = wTx + b where w = ( w0 ​ , w1 ​ , ... , wp ​ ) and x = ( x0 ​ , x1 ​ , ... , xp ​ ) . 3. Interpretation and Intuition The prediction is a linear combination of features — each feature contributes prop...

Predicting Probabilities

1. What is Predicting Probabilities? The predict_proba method estimates the probability that a given input belongs to each class. It returns values in the range [0, 1] , representing the model's confidence as probabilities. The sum of predicted probabilities across all classes for a sample is always 1 (i.e., they form a valid probability distribution). 2. Output Shape of predict_proba For binary classification , the shape of the output is (n_samples, 2) : Column 0: Probability of the sample belonging to the negative class. Column 1: Probability of the sample belonging to the positive class. For multiclass classification , the shape is (n_samples, n_classes) , with each column corresponding to the probability of the sample belonging to that class. 3. Interpretation of predict_proba Output The probability reflects how confidently the model believes a data point belongs to each class. For example, in ...

Supervised Learning

What is Supervised Learning? ·     Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) are paired with the correct output (labels). The model learns to map inputs to outputs and can predict labels for unseen input data. ·     Goal: To learn a function that generalizes well from training data to accurately predict labels for new data. ·          Types: ·          Classification: Predicting categorical labels (e.g., classifying iris flowers into species). ·          Regression: Predicting continuous values (e.g., predicting house prices). Key Concepts: ·     Generalization: The ability of a model to perform well on previously unseen data, not just the training data. ·         Overfitting and Underfitting: ·    ...

Kernelized Support Vector Machines

1. Introduction to SVMs Support Vector Machines (SVMs) are supervised learning algorithms primarily used for classification (and regression with SVR). They aim to find the optimal separating hyperplane that maximizes the margin between classes for linearly separable data. Basic (linear) SVMs operate in the original feature space, producing linear decision boundaries. 2. Limitations of Linear SVMs Linear SVMs have limited flexibility as their decision boundaries are hyperplanes. Many real-world problems require more complex, non-linear decision boundaries that linear SVM cannot provide. 3. Kernel Trick: Overcoming Non-linearity To allow non-linear decision boundaries, SVMs exploit the kernel trick . The kernel trick implicitly maps input data into a higher-dimensional feature space where linear separation might be possible, without explicitly performing the costly mapping . How the Kernel Trick Works: Instead of computing ...