JOSEPH T SHEN, M.D.
Medical Practice at Vanderventer Ave, Port Washington, NY

6709399, Mar 23, 2004

Oct 20, 2000

09/693071

Joseph T Shen - Sands Point NY
Shawn Dentler - Midvale UT

Cardiotran LCC
Premier Hear LLC - Port Washington NY

A61B 50402

600508, 128923

A method and system for the detection of heart disease in a patient, having a remote testing unit for gathering analog data concerning the status of a patients heart, converting the data to digital form, applying FFTs to the converted data, transferring the data to a computer for analysis. The analytic computer applies mathematical translation to the data received from the remote testing unit into a plurality of different graphical patterns representative of various states of the patients heart. A data base is also employed that has graphical patterns representative of various states of a plurality of patients hearts from normal through disease states. The analytic computer compares the graphical patterns of the patients heart against the data base of graphical patterns recognizes at least one pattern in the data base more similar to the graphical patterns of the patients heart. The analytic computer then determines a likely diagnosis of the patients heart as a result of the recognized patterns and outputs the likely diagnosis. Application of Internet and intranet solutions are also provided.

2002015, Oct 24, 2002

Apr 24, 2001

09/841559

Genquan Feng - New York NY, US
Joseph Shen - Sands Point NY, US
Li Feng - Elmhurst NY, US

A61B005/04

600/515000

Non-invasive to early detect myocardial inschemia is a very important problem in the medical profession. The invention used the method introduced in U.S. Pat. No. 5,509,425 and U.S. Pat. No. 5,649,544 to acquire positive (occurrence) indexes in every case of a sufficient amount database. Used the database empirically screening the said EMPI indexes, to select the CAD related indexes “Ic” and the NCI related indexes “In”, according to the positive rate of those indexes in the CAD patients and NCI patients which one is higher. Combined the “Ic” and “In” as “one Group” (Cluster), namely “Icn” to get the “index patterns” constructed by “Icn”, called “Pcn”. Then used the batabase empirically differentiating the “Pcn” to two Groups, one is “CAD related Group”, called “Pc”, and another is “NCI related Group”, called “Pn”, according to the positive rate of those “index patterns” in the CAD patients and NCI patients which one is higher. Then grossly identified the patient with the index pattern(s) within the scope of the Group “Pc” as a CAD patient, and grossly identified the patient as NCI patient when he has the index pattern(s) within the scope of the Group “Pn”. Finally, optimize the “gross diagnosis”, according to the principle of the method introduced in U.S. Pat. No. 5,542,429, to get final results (final differential diagnosis suggestions).