Context:

One of the clients wanted to classify whether a participant is awake or asleep at any given time using physiological signals (e.g., heart rate, EEG, movement) and behavioural data. The goal is to build a robust model using ensemble techniques and statistical validation.

Resolution:

We deployed a specialized team with deep knowledge that used combination of models to capture different patterns and Two-Tailed Hypothesis Test which were Null Hypothesis (H₀) and Alternative Hypothesis (H₁) on prediction accuracy vs baseline which resulted model being statistically significant.

This gave Real-time prediction of sleep state and Alerts for sleep disturbances or irregular patterns.

Result:

The ML-based approach made it easier where Neural networks excel at capturing temporal dependencies and Statistical validation confirms model reliability. The final dataset was accurate, well-organized, and ready for analysis—leading to better insights, faster decisions, and smoother reporting.