Apparatus and method for time series data format conversion and analysis
An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.
1 . An apparatus for time series data format conversion and analysis using machine learning, wherein the apparatus comprises:
at least a processor, and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:
receive a static image comprising a time series of measured values, wherein the at least a time series of measured values represents an electrocardiogram (ECG) of a subject;
iteratively train a generative machine learning model comprising a generator and a discriminator with training data comprising exemplary time series data and a plurality of synthetic static images, wherein the generator is configured to generate a synthetic static image to which the discriminator is configured to compare the synthetic static image to the training data for authenticity of a time series in a target domain, wherein a output of the discriminator is evaluated by a binary classifier configured to provide incorrect assessments by the discriminator as updates to the training data of the generative machine learning model;
convert the time series of measured values from the static image to a target domain protocol, wherein the conversion comprises:
parsing the time series of measured values to ECG data comprising data points representing the ECG of the subject,
wherein the data points represent lead signal and time and parsing the time series of measure values to ECG data comprises:
inputting the static image into the generative machine learning model; and
outputting, by the generative machine learning model, the ECG data;
instantiate a machine-learning model on an analytical circuit device, wherein the machine-learning model configures the analytical circuit device to perform an analytical process;
input the ECG data to the instantiated machine-learning model; and
output a diagnosis based on the ECG data.
2 . The apparatus of claim 1 , wherein:
the at least a time series further comprises a first time series including a first static image in a first static image format and a second time series in a second static image in a second static image format; and
the second static image format is distinct from the first static image format.
3 . The apparatus of claim 2 , wherein converting the at least a time series further comprises:
converting the first static image to a common domain protocol; and
converting the second static image to the common domain protocol.
4 . The apparatus of claim 1 , wherein the machine-learning model further comprises a diffusion-based machine-learning process.
5 . The apparatus of claim 1 , wherein the machine-learning model further comprises a generative machine-learning process using at least a deep neural network.
6 . The apparatus of claim 5 , wherein the at least a deep neural network includes a plurality of deep neural networks.
7 . The apparatus of claim 1 , wherein converting further comprises converting using a plurality of conditional inputs.
8 . The apparatus of claim 1 , further configured to:
retranslate the at least a time series from the target domain protocol to the at least an initial static image; and
validate the converted time series as a function of the retranslation.
9 . A method for time series data format conversion and analysis, wherein the method comprises:
receiving, by a computing device, a time series of measured values, wherein the time series of measured values is a static image;
iteratively training, by the computing device, a generative machine learning model comprising a generator and a discriminator with training data comprising exemplary time series data and a plurality of synthetic static images, wherein the generator is configured to generate a synthetic static image to which the discriminator is configured to compare the synthetic static image to the training data for authenticity of a time series in a target domain, wherein a output of the discriminator is evaluated by a binary classifier configured to provide incorrect assessments by the discriminator as updates to the training data of the generative machine learning model;
converting, by the computing device, the time series of measured values from the static image to a target domain protocol, wherein the conversion comprises:
parsing the time series of measured values to ECG data comprising data points representing the ECG of a subject, wherein the data points represent lead signal and time and parsing the time series of measure values to ECG data comprises:
inputting the static image into the generative machine learning model; and
outputting, by the generative machine learning model, the ECG data;
instantiating, by the computing device, a machine-learning model on an analytical circuit device, wherein the machine-learning model configures the analytical circuit device to perform an analytical process;
inputting, by the computing device, the ECG data to the instantiated machine-learning model; and
outputting, by the computing device, a diagnosis based on the ECG data.
10 . The method of claim 9 , wherein:
the at least a time series further comprises a first time series including a first static image in a first static image format and a second time series in a second static image in a second static image format; and
the second static image format is distinct from the first static image format.
11 . The method of claim 10 , wherein converting the at least a time series further comprises:
converting the first static image to a common domain protocol; and
converting the second static image to the common domain protocol.
12 . The method of claim 9 , wherein the generative machine learning model relies on machine-learning processes to iteratively map, by the computing device, an input to an output and comparatively eliminate cycle consistency losses as referenced against a concurrent mapping sequence.
13 . The method of claim 9 , wherein generative machine learning model further comprises a diffusion-based machine-learning process.
14 . The method of claim 9 , wherein the generative machine learning model further comprises a generative machine-learning process using at least a deep neural network.
15 . The method of claim 14 , wherein the at least a deep neural network includes a plurality of deep neural networks.
16 . The method of claim 6 , wherein converting further comprises converting using a plurality of conditional inputs.
17 . The method of claim 9 , further comprising:
retranslating the at least a time series from the target domain protocol to the at least an initial static image; and
validating the converted time series as a function of the retranslation.
18 . The method of claim 9 , wherein converting the time series between domains comprises converting, by the computing device, the time series between domains using diffusion models based on energy-guided stochastic calculus-based equations.
19 . The method of claim 9 , wherein receiving automated analysis of time series comprises receiving, by the computing device, the automated analysis of time series using a neural network of connected devices.
20 . The method of claim 9 , wherein receiving the automated analysis of time series comprises receiving, by the computing device, the automated analysis of time series from a scanned image relying on optical character recognition to interpret any available printed data.
21 . The method of claim 9 , wherein validating the conversion by reverse-translating the time series back to the original domain and verifying consistency is accomplished using machine-learning processes to assess, by the computing device, the translated data against a confidence threshold.
22 . The method of claim 9 , further comprising generating, using the computing device, a summary or recommendation based on the converted time series and an external machine-learning model.