
    Develop an algorithm to analyze ICU patient monitoring sequences where each health indicator reading is marked as either normal (0) or abnormal (1), and determine how many possible 2n-length monitoring patterns exist where the count of abnormal readings in the first n timepoints equals the count in the last n timepoints. This analysis helps identify balanced deterioration patterns where the frequency of abnormal readings remains consistent across two consecutive monitoring periods.
    

    
def count_binary_seq(n):
    """
    Develop an algorithm to analyze ICU patient monitoring sequences where each health indicator reading is marked as either normal (0) or abnormal (1), and determine how many possible 2n-length monitoring patterns exist where the count of abnormal readings in the first n timepoints equals the count in the last n timepoints. This analysis helps identify balanced deterioration patterns where the frequency of abnormal readings remains consistent across two consecutive monitoring periods.
    

    """
def count_binary_seq(n):
    """
    Develop an algorithm to analyze ICU patient monitoring sequences where each health indicator reading is marked as either normal (0) or abnormal (1), and determine how many possible 2n-length monitoring patterns exist where the count of abnormal readings in the first n timepoints equals the count in the last n timepoints. This analysis helps identify balanced deterioration patterns where the frequency of abnormal readings remains consistent across two consecutive monitoring periods.
    

    """
    nCr = 1
    res = 1
    for r in range(1, n + 1):
        nCr = nCr * (n + 1 - r) / r
        res += nCr * nCr
    return res