Apparatus and method for detecting hypertension attributes

An apparatus for detecting hypertension attributes in a patient time-series data, the apparatus comprising:

• at least a processor;
• and
• a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
• receive a patient time-series data associated with a patient, wherein the patient time-series data is captured using a measurement device, communicatively connected to the at least a processor, comprising at least a transducer wherein the transducer operates as a low-pass filter, responding to input stimuli with a delay and filtering signals above a maximum detectable frequency determined by the transducer's response time;
• generate one or more vectors from the patient time-series data, wherein each vector represents quantitative values associated with hypertension attributes, map the one or more vectors into a vector space, and organize the one or more vectors into a matrix used by a plurality of hypertension models;
• input the matrix of patient time-series data into a hypertension panel, wherein the hypertension panel comprises the plurality of hypertension models further comprising at least a first hypertension model and a second hypertension model;
• and
• generate a hypertension attribute from the hypertension panel as a function of the patient time-series data and the hypertension panel, wherein the hypertension attribute comprises a probability of the patient developing a future heart condition, wherein generating the hypertension attribute comprises:
• generating training data for the plurality of hypertension models in the hypertension panel based on previous iterations of receiving patient time-series data using the measurement device;
• training the first hypertension model and the second hypertension model with the training data;
• generating, using the first hypertension model, a first hypertension attribute comprising a measurement of the patient yielding a first hypertension level using the patient time-series data;
• and
• generating, using the second hypertension model, a second hypertension attribute using the patient time-series data.