Available Technology
Non-subjective, diagnosis of depression
Technology:
Method to diagnose major depressive disorder by examining the entropic complexity of inter-beat heart signals.
Markets Addressed
Major depressive disorder (MDD) is a major public health problem affecting over 121 million worldwide and generating sales of over $10 billion in drug treatments. The impact of MDD spans from decreased quality of life to economic costs from health care expenditures and from lowered productivity to increased risk of cardiovascular disease.
Clinical diagnosis and monitoring of MDD and its therapy are currently based exclusively on subjective criteria, such as the Hamilton scoring index. The search for objective, reliable biomarkers of MDD remains a central challenge in contemporary medicine. The method presented herein represents the first physiological means by which MDD can be diagnosed.
Innovations and Advantages
This method takes advantage of the fact that MDD alters the complexity of a number of physiologic output signals. These signals include the heartbeat time series obtained from a continuous electrocardiogram during sleeping hours, the changes in the local energy of voice dynamics and the changes in sleep stage fluctuations. In all three contexts, MDD lowers the degree of complexity of these signals, as measured using multiscale entropy (MSE) and related techniques that probe the information content of a signal across multiple time scales. This approach will serve as the basis of making the diagnosis of MDD, of tracking its response to therapy and of evaluating therapeutic interventions, both pharmacologic and non-pharmacologic.
This technology represents a major step forward in the diagnosis of MDD and is likely to correct a broader swath of patients who may not be willing to receive behavioral exams. With a software solution integrated within a standard ECG machine or running on a dedicated PC, MDD diagnosis can be made quickly and easily without stigma.
Additional Information
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Inventor(s):
Costa, Madalena Damasio
Goldberger, Ary Louis
Categories:
For further information, please contact:
Alan Gordon, Director of Business Development
(617) 384-5000
Reference Harvard Case #4215