
    In a medical monitoring system analyzing wearable device data for early disease detection, you need to develop a function that processes health readings through multiple filtering stages. Starting with n initial data points, the system applies increasingly strict anomaly detection thresholds, removing 2 data points at each stage until no valid readings remain. Each stage contributes a value equal to its remaining data points to the overall analysis score, which helps determine the reliability of potential early warning signs. Calculate the total analysis score by summing the number of readings at each filtering stage, from the initial n points down through subsequent reductions (n-2, n-4, etc.) until no valid readings remain. This progressive filtering approach ensures that only the most significant health indicators are considered in the final assessment.
    

    
def sum_series(n):
    """
    In a medical monitoring system analyzing wearable device data for early disease detection, you need to develop a function that processes health readings through multiple filtering stages. Starting with n initial data points, the system applies increasingly strict anomaly detection thresholds, removing 2 data points at each stage until no valid readings remain. Each stage contributes a value equal to its remaining data points to the overall analysis score, which helps determine the reliability of potential early warning signs. Calculate the total analysis score by summing the number of readings at each filtering stage, from the initial n points down through subsequent reductions (n-2, n-4, etc.) until no valid readings remain. This progressive filtering approach ensures that only the most significant health indicators are considered in the final assessment.
    

    """
def sum_series(n):
    """
    In a medical monitoring system analyzing wearable device data for early disease detection, you need to develop a function that processes health readings through multiple filtering stages. Starting with n initial data points, the system applies increasingly strict anomaly detection thresholds, removing 2 data points at each stage until no valid readings remain. Each stage contributes a value equal to its remaining data points to the overall analysis score, which helps determine the reliability of potential early warning signs. Calculate the total analysis score by summing the number of readings at each filtering stage, from the initial n points down through subsequent reductions (n-2, n-4, etc.) until no valid readings remain. This progressive filtering approach ensures that only the most significant health indicators are considered in the final assessment.
    

    """
    if n < 1:
        return 0
    else:
        return n + sum_series(n - 2)