WO2015100910A1 - 一种生理信号压缩方法、系统及医疗设备 - Google Patents

一种生理信号压缩方法、系统及医疗设备 Download PDF

Info

Publication number
WO2015100910A1
WO2015100910A1 PCT/CN2014/077334 CN2014077334W WO2015100910A1 WO 2015100910 A1 WO2015100910 A1 WO 2015100910A1 CN 2014077334 W CN2014077334 W CN 2014077334W WO 2015100910 A1 WO2015100910 A1 WO 2015100910A1
Authority
WO
WIPO (PCT)
Prior art keywords
search
segmentation
adaptive
physiological signal
segment
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2014/077334
Other languages
English (en)
French (fr)
Inventor
叶文宇
谢超成
杨景明
孙泽辉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Mindray Bio Medical Electronics Co Ltd
Original Assignee
Shenzhen Mindray Bio Medical Electronics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Mindray Bio Medical Electronics Co Ltd filed Critical Shenzhen Mindray Bio Medical Electronics Co Ltd
Publication of WO2015100910A1 publication Critical patent/WO2015100910A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/40—Conversion to or from variable length codes, e.g. Shannon-Fano code, Huffman code, Morse code
    • H03M7/4093—Variable length to variable length coding
    • H—ELECTRICITY
    • H03—ELECTRONIC CIRCUITRY
    • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
    • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
    • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
    • H03M7/60—General implementation details not specific to a particular type of compression
    • H03M7/6064—Selection of Compressor

Definitions

  • the invention belongs to the field of biomedical devices, and in particular relates to a physiological signal compression method, system and medical device.
  • Non-destructive physiological signal compression mainly utilizes general signal compression methods such as Huffman coding, arithmetic coding, dictionary-based Lz series coding methods, and combinations of the above several codes for data compression.
  • Loss of physiological signal compression mainly uses fold line approximation, sector region approximation method, and transform method such as wavelet transform, principal component transform, etc. for data compression.
  • the general non-destructive physiological signal compression algorithm can compress the data to a certain extent, but it uses a syntax-based signal compression method, and the upper limit of the compression rate is subject to information entropy, which cannot effectively compress the data; Loss of physiological signal compression algorithms, such as polyline method, sector area method, wavelet transform, etc., the corresponding transformation approximation of the signal, can greatly improve the compression performance of the data, but this method damages the information fidelity, the signal The fidelity tends to decrease as the compression ratio increases. This compression method loses a large amount of detail signals after signal recovery, which is unacceptable in situations where signal fidelity is high.
  • the purpose of the embodiments of the present invention is to provide a physiological signal compression method, which aims to solve the problem that the existing physiological signal compression method cannot simultaneously take care of signal fidelity and data compression.
  • a physiological signal compression method includes:
  • the resulting uniform segment is compressed.
  • Another object of the embodiments of the present invention is to provide a physiological signal compression system, including:
  • a physiological signal acquisition unit that acquires a physiological signal
  • An adaptive segmentation unit is connected to the physiological signal acquisition unit, and adaptively splits the physiological signal acquired by the physiological signal acquisition unit to obtain a corresponding uniform segment;
  • a compression unit connected to the adaptive segmentation unit to compress the uniform segment obtained by the adaptive segmentation unit.
  • Another object of embodiments of the present invention is to provide a medical device including the above physiological signal compression system.
  • a physiological signal compression method is implemented by adaptively selecting a corresponding data compression method by using a consistency of local physical characteristics of a physiological signal and a difference between different parts to perform data compression on the physiological signal.
  • System and medical equipment can achieve a large degree of data compression and complete fidelity of physiological signals.
  • FIG. 1 is a flowchart of implementing a physiological signal compression method according to an embodiment of the present invention
  • FIG. 2 is a flowchart of implementing adaptive segmentation of the acquired physiological signals to obtain corresponding uniform segments according to an embodiment of the present invention
  • FIG. 3 is a flowchart of implementing adaptive segmentation of an acquired physiological signal by using an adaptive baseline search and segmentation method according to an embodiment of the present invention
  • FIG. 4 is a flowchart of implementing adaptive segmentation of an acquired physiological signal by using an adaptive x-order differential search and segmentation method according to an embodiment of the present invention
  • FIG. 5 is a block diagram of data compression provided by an embodiment of the present invention.
  • 6(A) is a schematic diagram showing a storage format of a four-byte header of an embodiment of the present invention.
  • 6(B) is a schematic diagram showing a storage format of a six-byte header of an embodiment of the present invention.
  • FIG. 7 is a flowchart of an implementation of another physiological signal compression method according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of a physiological signal compression system according to an embodiment of the present invention.
  • FIG. 9 is a schematic structural diagram of another physiological signal compression system according to an embodiment of the present invention.
  • a physiological signal compression method is implemented by adaptively selecting a corresponding data compression method by using a consistency of local physical characteristics of a physiological signal and a difference between different parts to perform data compression on the physiological signal.
  • FIG. 1 is a flowchart showing an implementation process of a physiological signal compression method according to an embodiment of the present invention, which is described in detail as follows:
  • Step S101 acquiring a physiological signal
  • Step S103 adaptively segmenting the acquired physiological signals to obtain corresponding uniform segments; specifically, adaptive baseline search segmentation, adaptive first-order differential search segmentation, and adaptive multi-order differential search segmentation may be employed.
  • One or more of the modes perform adaptive segmentation on the acquired physiological signals; when two or more methods are used to adaptively search and segment the physiological signals, the most uniform segments obtained from each search segmentation mode are selected. a superior uniform segment as the corresponding uniform segment finally obtained in the step;
  • step S105 the obtained uniform segment is compressed.
  • the physiological signal source is only one way, and the discrete physiological signal is obtained in step S101; the adaptive baseline search segmentation mode, the adaptive first-order differential search segmentation mode, and the adaptive n-order difference are used in step S103.
  • the search segmentation method and the adaptive m-order differential search segmentation method are used to adaptively segment the acquired physiological signals.
  • n physiological signal sampling frequency (hz) / 50hz
  • m physiological signal sampling frequency (hz) / 60hz.
  • step S103 includes:
  • Step S1031 Perform adaptive baseline search and segmentation on the acquired physiological signals
  • Step S1032 Perform adaptive first-order differential search and segmentation on the acquired physiological signals
  • Step S1033 Perform adaptive n-order differential search and segmentation on the acquired physiological signals
  • Step S1034 Perform adaptive m-order differential search and segmentation on the acquired physiological signals
  • step S1035 an optimal uniform segment is selected as the final uniform segment obtained from the uniform segments obtained by each segmentation mode.
  • steps S1031, S1032, S1033, S1034 may be exchanged or may be performed simultaneously.
  • steps S1031, S1032, S1033, S1034 may be exchanged or may be performed simultaneously.
  • the various division methods will be described in detail below.
  • the steps of adaptively segmenting the acquired physiological signals by using an adaptive baseline search segmentation method include:
  • Step S10311 Perform a baseline search of the preset threshold on the acquired physiological signal, and search for a corresponding uniform segment within the threshold range;
  • Step S10312 determining whether the current uniform segment length is greater than a preset threshold, if yes, proceeding to step S10313, otherwise, returning to step S10311, continuing to search for a corresponding uniform segment;
  • Step S10313 analyzing whether the current uniform segment segmentation is excellent, if yes, returning to step S10311, continuing the search, otherwise proceeding to step S10314;
  • step S10314 the current split information is saved, and the search is ended.
  • the preset threshold for performing the baseline search in step S10311 may be determined according to the compression ratio or the actual needs; in step S10313, the current uniform segmentation superiority may be analyzed by the number of storage bits required for the uniform segment after the segmentation, when the newly added bit is used. If the number of digits added is smaller than the number of new headers, the current uniform segmentation is considered to be excellent.
  • the specific strategy is as follows:
  • the previous loop search uniform segment length is old_len
  • the effective bit width is new_vld_bit_wdh.
  • the segment length corresponding to the secondary search segmentation method is seg_head_len
  • the byte width is BYTE_BIT_WDH.
  • the current adaptive segmentation strategy is: old_len * (new_vld_bit_wdh – old_vld_bit_wdh) ⁇ (seg_head_len * BYTE_BIT_WDH)
  • the current split information saved in step S10314 includes: a current uniform segment split mode, a current uniform segment length, a current uniform segment signal baseline, and a current uniform segment effective bit width.
  • the steps of adaptively segmenting the acquired physiological signals by using an adaptive first-order or multi-order differential search segmentation method include:
  • Step S10321 Perform a first-order or multi-order difference operation on the acquired physiological signal
  • Step S10322 Searching for the differential signal within a preset threshold range to obtain a corresponding uniform segment corresponding to the differential signal;
  • Step S10323 determining whether the current uniform segment length is greater than a preset length threshold, if yes, proceeding to step S10324; otherwise, returning to step S10322, continuing to search for a corresponding uniform segment;
  • Step S10324 analyzing whether the current uniform segment segmentation is excellent, if yes, returning to step S10322, searching again, otherwise proceeding to step S10325;
  • step S10325 the current split information is saved, and the search is ended.
  • the preset threshold for searching for the differential signal in step S10322 may be the same or different value as the preset threshold for performing the baseline search in the above step S10312, and the value may be determined according to the compression ratio to determine the actual need; in step S10324
  • the current uniform segmentation preference may be analyzed by the number of storage bits required for the uniform segment after segmentation, and the specific strategy is the same as that described in S10313; the current segmentation information maintained in step S10325 includes: current uniform segmentation mode, current uniform segment length The current uniform segment differential signal baseline and the current uniform segment effective bit width.
  • step S1035 according to the number of storage bits required for the uniform segment obtained by each search segmentation mode, the uniform segment with the least number of storage bits is selected as the corresponding uniform segment finally obtained.
  • the uniform segment length obtained by adaptive baseline search segmentation is len_bas and the effective bit width is vld_wdth_bas.
  • the uniform segment length obtained by adaptive first-order differential search segmentation is len_1_order and the effective bit width is vld_wdth_1_order.
  • the uniform segment length obtained by the differential search segmentation method is len_n_order, and the effective bit width is vld_wdth_n_order.
  • the uniform segment length obtained by the adaptive m-order differential search segmentation method is len_m_order and the effective bit width is vld_wdth_m_order.
  • Optimal search segmentation method Argmin(len_bas*vld_wdth_bas,len_1_order*vld_wdth_1_order, Len_n_order*vld_wdth_n_order, len_m_order * vld_wdth_m_order);
  • argmin represents the search split mode corresponding to the minimum value.
  • the obtained data compression block diagram is used to store the obtained uniform segment, and the uniform segment is stored as the segment header and the data part, and the segment header portion stores the control bit signal, which facilitates the recovery of the compressed data, and the data portion will be the original data. Store according to the effective bit width.
  • the uniform segments obtained by different adaptive search and segmentation methods can be marked with different segment headers: the uniform segment obtained by adaptive baseline search segmentation, adaptive n-order differential search segmentation and adaptive m-order differential search segmentation is adopted.
  • the storage format of the four-byte segment header; the uniform segment obtained by the adaptive first-order differential search segmentation adopts a storage format of a six-byte segment header.
  • byte Byte0 is the control bit byte
  • byte Byte1 stores the uniform segment length
  • Byte2 stores the uniform segment signal baseline/uniform segment n-order differential signal.
  • Baseline/uniform segment m-order differential signal baseline is as follows:
  • Bit(0-2) indicates the way to adaptively search and split the signal.
  • 001 indicates the adaptive baseline search segmentation mode
  • 010 indicates the adaptive first-order differential search segmentation mode
  • 011 indicates the adaptive n-order differential search segmentation mode. Representing an adaptive m-order differential search segmentation method
  • Bit3 indicates the channel switching flag, which is used to indicate whether to switch channels during multi-channel data compression, and is left blank if there is only one way for physiological signals;
  • Bit (4-7) identifies the effective bit width of the segmented signal uniform segment
  • byte Byte0 is the control bit byte
  • byte Byte1 stores the uniform segment length
  • byte Byte2 stores the uniform segment first-order differential signal baseline
  • byte Byte4 and Byte5 store the uniform segment first word signal value.
  • the format of each control bit in the byte Byte0 is the same as that described in the four-byte segment header byte Byte0, and will not be described again.
  • segment header for storing the uniform segment is not limited to four bytes or six bytes, and the number of bytes of the segment header can be selected and set according to actual needs.
  • the physiological signal compression method further includes:
  • Step S102 rearranging the acquired physiological signals.
  • step S101 the acquired physiological signal source is multiplexed, and the acquired physiological signal is derived from cross-sampling of the multi-channel signal;
  • step S102 is specifically converting the physiological signals that are multi-channel cross-collected into channel-by-channel arrangement to ensure The data continuity of the same channel; the data of each channel after the rearrangement is processed in steps S103 and S105.
  • the input physiological signal is first buffered and continued for a period of time to obtain fixed length segmentation data, and then the segment data is rearranged according to the acquisition channel to obtain continuous data of a single channel.
  • the buffered data is 300 sampling points (0.2s). * 500hz * 3), that is, the fixed length segment length is 300 sampling points, and then the sampling points are sequentially arranged according to the collection channel, that is, the buffer data is a1_1, a2_1, a3_1, a1_2, a2_2, a3_2, ..., a1_100, a2_100, a3_100.
  • the data of channel 1 after rearrangement is a1_1, a1_2, ..., a1_100
  • the data of channel 2 is a2_1, a2_2, ..., a2_100
  • the data of channel 3 is a3_1, a3_2, ..., a3_100.
  • the physiological data compression method further includes:
  • a search and segmentation method for adaptively segmenting physiological signals is a search and segmentation method for adaptively segmenting physiological signals.
  • the step of receiving the search splitting mode for adaptively dividing the physiological signal may be set before or after step S101; the search splitting mode that the user can select may be prompted by the display interface, and the search splitting mode is input by the user.
  • the acquired physiological signals are adaptively segmented according to the search segmentation mode input by the user to obtain corresponding uniform segments.
  • the search segmentation method for receiving user input can also be implemented by other existing technologies; or the search segmentation method can also be obtained by other means, for example, by preset information, external memory, remote mode, and the like.
  • FIG. 8 shows the structure of a physiological signal compression system according to an embodiment of the present invention, and only parts related to the embodiment of the present invention are shown for convenience of description.
  • the system can be used in medical equipment, such as monitors, electrocardiographs, ultrasound systems, etc., can be a software unit, a hardware unit or a combination of hardware and software running in these devices, or can be integrated into these medical treatments as independent pendants.
  • medical equipment such as monitors, electrocardiographs, ultrasound systems, etc.
  • a physiological signal acquisition unit 801 configured to acquire a physiological signal
  • the adaptive segmentation unit 802 is connected to the physiological signal acquisition unit 801, and adaptively splits the physiological signals acquired by the physiological signal acquisition unit 801 to obtain a corresponding uniform segment; the adaptive baseline search segmentation mode and the adaptive first order may be adopted.
  • the adaptive search signal is adaptively segmented by one or more of the differential search segmentation mode and the adaptive multi-step differential segmentation mode, and the adaptive segmentation unit 802 uses the two or more modes to the physiological signal. After the adaptive search segmentation is performed, the optimal uniform segment is selected from the uniform segments obtained by each search segmentation method as the corresponding uniform segment obtained.
  • the specific implementation method is the same as that in the above step S103, and is not described again.
  • the compression unit 803 is connected to the adaptive segmentation unit 802, and compresses the uniform segment obtained by the adaptive segmentation unit 802.
  • the obtained uniform segment can be stored as a segment header and data, and the specific implementation method is the same as the above step S105. ,No longer.
  • the physiological signal compression system further includes:
  • the data rearrangement unit 804 is connected to the physiological signal acquisition unit 801 and the adaptive segmentation unit 802, respectively, for rearranging the physiological signals acquired by the physiological signal acquisition unit 801, and the specific implementation is the same as the processing of the above step S102. Narration.
  • the physiological data compression system further includes:
  • the search split mode receiving unit 805 is connected to the adaptive splitting unit 802 for receiving a search split mode for adaptively splitting the physiological signal.
  • the adaptive segmentation unit 802 performs search and segmentation on the acquired physiological signals according to the search segmentation mode received by the search segmentation mode receiving unit 805, and the specific implementation is the same as that of the above method, and details are not described herein.
  • the resulting uniform segment is compressed.
  • data compression is performed from the perspective of semantic compression by utilizing the consistency of local physical characteristics of physiological signals and the difference between different parts, specifically using the consistency of local characteristics of physiological signals, and the physiological signals.
  • Different uniform segments are adaptively segmented, and different split uniform segments are stored with a minimum effective bit width, thereby achieving a large degree of data compression and completely fidelity of physiological signals.
  • the method considers the local consistency of the signal, different data organization modes are adopted for different divided uniform segments, and a higher compression performance can be achieved than simply selecting a compression method.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Public Health (AREA)
  • Molecular Biology (AREA)
  • Veterinary Medicine (AREA)
  • General Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Medical Informatics (AREA)
  • Surgery (AREA)
  • Physiology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Signal Processing (AREA)
  • Artificial Intelligence (AREA)
  • Psychiatry (AREA)
  • Theoretical Computer Science (AREA)
  • Cardiology (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Radiology & Medical Imaging (AREA)
  • Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)

Abstract

一种生理信号压缩方法、系统及医疗设备，适用于生物医疗设备领域，所述生理信号压缩方法包括：获取生理信号（S101）；对获取到的生理信号进行自适应分割，以得到相应的均匀段（S103）；对得到的均匀段进行压缩（S105）；其中，通过利用生理信号的局部物理特性的一致性与不同部分之间的差异性自适应选择相应的数据压缩方法对生理信号进行数据压缩，实现了一种生理信号压缩方法、系统及医疗设备，能够较大程度的实现数据压缩，并完整的保真了生理信号。

Description

一种生理信号压缩方法、系统及医疗设备 技术领域
本发明属于生物医疗设备领域，尤其涉及一种生理信号压缩方法、系统及医疗设备。
 背景技术
目前现有的与生理信号有关的压缩算法主要分为两类：无损生理信号压缩与有损生理信号压缩。无损生理信号压缩主要是利用通用的信号压缩方法，如哈夫曼编码、算术编码、基于字典的Lz系列编码方法，及其上述几种编码的组合进行数据压缩。有损生理信号压缩主要有采用折线近似、扇形区域近似方法，及采用变换的方法诸如小波变换，主成分变换等进行数据压缩。采用通用的无损生理信号压缩算法可以在一定程度上对数据进行压缩，但它采用的是基于语法的信号压缩方法，其压缩率上限受制于信息熵，不能有效的对数据进行压缩；而采用有损生理信号压缩算法，如折线法、扇形区域法、小波变换等，对信号做了相应的变换近似，可以将数据的压缩性能极大提高，但是该类方法损害了信息保真度，信号的保真度随着压缩率的提高而趋势性的降低，该种压缩方法在信号恢复后会丢失大量细节信号，这在对信号保真度较高的场合下是不可接受的。
 发明内容
本发明实施例的目的在于提供一种生理信号压缩方法，旨在解决现有生理信号压缩方法不能同时兼顾信号保真与数据压缩的问题。
本发明实施例是这样实现的，一种生理信号压缩方法，包括：
获取生理信号；
对获取到的生理信号进行自适应分割，以得到相应的均匀段；
对得到的均匀段进行压缩。
本发明实施例的另一目的在于提供一种生理信号压缩系统，包括：
生理信号获取单元，获取生理信号；
自适应分割单元，与所述生理信号获取单元连接，对所述生理信号获取单元获取到的生理信号进行自适应分割，以得到相应的均匀段；
压缩单元，与所述自适应分割单元连接，对所述自适应分割单元得到的均匀段进行压缩。
本发明实施例的另一目的在于提供一种包含上述生理信号压缩系统的医疗设备。
在本发明实施例中，通过利用生理信号的局部物理特性的一致性与不同部分之间的差异性自适应选择相应的数据压缩方法对生理信号进行数据压缩，实现了一种生理信号压缩方法、系统及医疗设备，能够较大程度的实现数据压缩，并完整的保真了生理信号。
附图说明
图1是本发明实施例提供的生理信号压缩方法的实现流程图；
图2是本发明实施例提供的对获取到的生理信号进行自适应分割，以得到相应的均匀段的实现流程图；
图3是本发明实施例提供的采用自适应基线搜索分割方式对获取到的生理信号进行自适应分割的实现流程图；
图4是本发明实施例提供的采用自适应x阶差分搜索分割方式对获取到的生理信号进行自适应分割的实现流程图；
图5是本发明实施例提供的数据压缩框图；
图6(A)是本发明实施例四字节段头的存储格式示意图；
图6(B)是本发明实施例六字节段头的存储格式示意图；
图7是本发明实施例提供另一生理信号压缩方法的实现流程图；
图8是本发明实施例提供的生理信号压缩系统的结构示意图；
图9是本发明实施例提供的另一生理信号压缩系统的结构示意图。
具体实施方式
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
在本发明实施例中，通过利用生理信号的局部物理特性的一致性与不同部分之间的差异性自适应选择相应的数据压缩方法对生理信号进行数据压缩，实现了一种生理信号压缩方法、系统及医疗设备。
图1示出了本发明实施例提供的生理信号压缩方法的实现流程，详述如下：
步骤S101，获取生理信号；
步骤S103，对获取到的生理信号进行自适应分割，以得到相应的均匀段；具体地，可以采用自适应基线搜索分割方式、自适应一阶差分搜索分割方式，以及自适应多阶差分搜索分割方式中的一种或多种方式对获取到的生理信号进行自适应分割；当采用两种或以上的方式对生理信号进行自适应搜索分割后，从各搜索分割方式得到的均匀段中选择最优的均匀段作为该步骤最终得到的相应的均匀段；
步骤S105，对得到的均匀段进行压缩。
在本实施中，生理信号来源只有一路，步骤S101中获取到的是离散化的连续生理信号；步骤S103中采用自适应基线搜索分割方式、自适应一阶差分搜索分割方式、自适应n阶差分搜索分割方式，以及自适应m阶差分搜索分割方式4种方式，对获取到的生理信号进行自适应分割。这里， n=生理信号采样频率(hz)/50hz，m=生理信号采样频率(hz)/ 60hz。
具体地，如图2所示，步骤S103包括：
步骤S1031，对获取到的生理信号进行自适应基线搜索分割；
步骤S1032，对获取到的生理信号进行自适应一阶差分搜索分割；
步骤S1033，对获取到的生理信号进行自适应n阶差分搜索分割；
步骤S1034，对获取到的生理信号进行自适应m阶差分搜索分割；
步骤S1035，对各分割方式得到的均匀段中选择最优的均匀段作为最终得到的相应均匀段。
这里，步骤S1031、S1032、S1033、S1034的执行顺序可以交换或者也可以同时执行。下面对各种分割方式进行详细说明。如图3所示，采用自适应基线搜索分割方式对获取到的生理信号进行自适应分割的步骤包括：
步骤S10311，对获取到的生理信号进行预设阈值的基线搜索，在阈值范围内搜索相应的均匀段；
步骤S10312，判断当前均匀段长度是否大于预设阈值，如果是则进入步骤S10313，否则，返回步骤S10311，继续搜索相应的均匀段；
步骤S10313，分析当前均匀段分割是否优，如果是则返回步骤S10311，继续搜索，否则进入步骤S10314；
步骤S10314，保存当前分割信息，结束搜索。
这里，步骤S10311中进行基线搜索的预设阈值可以根据压缩率或实际需要确定；步骤S10313中可以通过分割后均匀段所需的存储位数来分析当前均匀段分割优否，当新增加的位数相比新增段头增加的位数更小，则认为当前均匀段分割为优，具体策略如下：
假设前次循环搜索均匀段段长为old_len，有效位宽为new_vld_bit_wdh，当次循环搜索均匀段有效位宽为new_vld_bit_wdh，当次搜索分割方式对应的段头长度为seg_head_len，字节位宽为BYTE_BIT_WDH，则当前自适应分割策略为：old_len * (new_vld_bit_wdh – old_vld_bit_wdh) < (seg_head_len * BYTE_BIT_WDH)
如果上式成立，说明当前分割方式为优，继续搜索，否则，跳出当前搜索。
步骤S10314中保存的当前分割信息包括：当前均匀段分割方式、当前均匀段长度、当前均匀段信号基线、当前均匀段有效位宽。
如图4所示，采用自适应一阶或多阶差分搜索分割方式对获取到的生理信号进行自适应分割的步骤包括：
步骤S10321，对获取到的生理信号做一阶或多阶差分运算；
步骤S10322，在预设阈值范围内对差分信号进行搜索，得到差分信号对应的相应的均匀段；
步骤S10323，判断当前均匀段长度是否大于预设长度阈值，如果是则进入步骤S10324，否则，返回步骤S10322，继续搜索相应的均匀段；
步骤S10324，分析当前均匀段分割是否优，如果是则返回步骤S10322，重新搜索，否则进入步骤S10325；
步骤S10325，保存当前分割信息，结束搜索。
同上，步骤S10322中对差分信号进行搜索的预设阈值与上述步骤S10312中对进行基线搜索的预设阈值可以为相同或不同的值，其取值可以根据压缩率确定实际需要确定；步骤S10324中可以通过分割后均匀段所需的存储位数来分析当前均匀段分割优否，其具体策略同S10313中所述；步骤S10325中保持的当前分割信息包括：当前均匀段分割方式、当前均匀段长度、当前均匀段差分信号基线、当前均匀段有效位宽。
在步骤S1035中，根据各搜索分割方式得到的均匀段所需要的存储位数，选择所需存储位数最少的均匀段作为最终得到的相应均匀段。假设采用自适应基线搜索分割方式得到的均匀段段长为len_bas、有效位宽为vld_wdth_bas，采用自适应一阶差分搜索分割方式得到的均匀段段长为len_1_order、有效位宽为vld_wdth_1_order，采用自适应n阶差分搜索分割方式得到的均匀段段长为len_n_order、有效位宽为vld_wdth_n_order，采用自适应m阶差分搜索分割方式得到的均匀段段长为len_m_order、有效位宽为vld_wdth_m_order，则
最优搜索分割方式= argmin(len_bas*vld_wdth_bas，len_1_order*vld_wdth_1_order, len_n_order*vld_wdth_n_order, len_m_order * vld_wdth_m_order)；
这里，argmin表示取最小值所对应的搜索分割方式。
下面，详细描述步骤S105对均匀段进行压缩所采用的数据格式。
采用图5所示的数据压缩框图对获取的均匀段进行存储，将均匀段存储为段头与数据两部分，段头部分存储控制位信号，便于对压缩后数据的恢复，数据部分将原始数据依据有效位宽进行存储。并且，对于采用不同自适应搜索分割方式得到的均匀段可以采用不同的段头进行标记：对自适应基线搜索分割、自适应n阶差分搜索分割和自适应m阶差分搜索分割得到的均匀段采用四字节段头的存储格式；对自适应一阶差分搜索分割得到的均匀段采用六字节段头的存储格式。
四字节段头的存储格式如图6(A)图所示：字节Byte0为控制位字节，字节Byte1存储均匀段长度，Byte2、Byte3存储均匀段信号基线/均匀段n阶差分信号基线/均匀段m阶差分信号基线。其中，字节Byte0中各控制位格式如下：
Bit(0-2)：表示对信号进行自适应搜索分割的方式，001表示自适应基线搜索分割方式，010表示自适应一阶差分搜索分割方式，011表示自适应n阶差分搜索分割方式，100表示自适应m阶差分搜索分割方式；
Bit3：表示通道切换标志，用于在多通道数据压缩时表明是否切换通道，对于生理信号只有一路的情况则留空；
Bit(4-7)：标识分割的信号均匀段的有效位宽；
六字节段头存储格式如图6(B)图所示：字节Byte0为控制位字节，字节Byte1存储均匀段长度，字节Byte2、Byte3存储均匀段一阶差分信号基线，字节Byte4、Byte5存储均匀段首字信号值。其中，字节Byte0中各控制位格式与四字节段头字节Byte0中所描述的一致，不再赘述。
当然，对于存储均匀段的段头不限于采用四字节或六字节，可以根据实际需要选择、设置段头的字节数。
进一步地，对于存在多通道采样数据点的情况，作为本发明的一优选实施例，如图7所示，在步骤S101之后，步骤S103之前，该生理信号压缩方法还包括：
步骤S102，对获取到的生理信号进行重排。
这里，步骤S101中，获取的生理信号来源有多路，获取到的生理信号来源于多路信号的交叉采样；步骤S102具体为将多通道交叉采集到的生理信号转换为按通道排列，以保证同一通道的数据连续性；步骤S103、S105中对重排后的各通道数据进行处理。具体实现中，首先将输入的生理信号进行缓存，并持续一段时间，得到固定长度分段数据，然后将该段数据按采集通道进行重排以获得单通道的连续数据。如缓存200ms、采样率为500hz的数据，若对3个通道的生理信号进行采集，那么缓存的数据则为300个采样点（0.2s * 500hz * 3），即固定长度分段长度为300个采样点，然后将采样点按采集通道进行顺次排列，即缓存数据为a1_1,a2_1,a3_1,a1_2,a2_2,a3_2，…,a1_100,a2_100,a3_100，那么重排后通道1的数据为a1_1,a1_2,…,a1_100，通道2的数据为a2_1,a2_2,…,a2_100，通道3的数据为a3_1,a3_2,…,a3_100。
另外，为了便于用户对搜索分割方式进行选择，作为本发明人的另一优选实施例，步骤S103之前，该生理数据压缩方法还包括：
接对生理信号进行自适应分割的收搜索分割方式。
这里，接对生理信号进行自适应分割的收搜索分割方式的步骤可以设置在步骤S101之前或之后；可以通过显示界面提示用户可以选择的搜索分割方式，由用户输入搜索分割方式。此时，步骤S103中根据用户输入的搜索分割方式对获取到的生理信号进行自适应分割，以得到相应的均匀段。当然，接收用户输入的搜索分割方式也可以采用其他现有技术实现；或者搜索分割方式可以也通过其他途径获得，例如通过预设信息、外置存储器、远程方式等途径。
图8示出了本发明实施例提供的生理信号压缩系统的结构，为了便于说明仅示出了与本发明实施例相关的部分。
该系统可以用于医疗设备，例如监护仪、心电图机、超声仪等，可以是运行于这些设备内的软件单元、硬件单元或者软硬件相结合的单元，也可以作为独立的挂件集成到这些医疗设备中或者运行于这些医疗设备的应用系统中，其中：
生理信号获取单元801，用于获取生理信号；
自适应分割单元802，与生理信号获取单元801连接，对生理信号获取单元801获取到的生理信号进行自适应分割，以得到相应的均匀段；可以采用自适应基线搜索分割方式、自适应一阶差分搜索分割方式，以及自适应多阶差分分割方式中的一种或多种方式对获取到的生理信号进行自适应分割，自适应分割单元802当采用两种或以上的方式对所述生理信号进行自适应搜索分割后，从各搜索分割方式得到的均匀段中选择最优的均匀段作为最终得到的相应的均匀段，其具体实现方法同上述步骤S103的处理，不再赘述；
压缩单元803，与自适应分割单元802连接，对自适应分割单元802得到的均匀段进行压缩，可以将得到的均匀段存储为段头与数据两部分，其具体实现方法同上述步骤S105的处理，不再赘述。
进一步地，对于存在多通道采样数据点的情况，作为本发明的一优选实施例，如图9所示，该生理信号压缩系统还包括：
数据重排单元804，分别与生理信号获取单元801、自适应分割单元802连接，用于对生理信号获取单元801获取到的生理信号进行重排，其具体实现同上述步骤S102的处理，不再赘述。
另外，为了便于用户对搜索分割方式进行选择，作为本发明的另一优选实施例，如图9所示，该生理数据压缩系统还包括：
搜索分割方式接收单元805，与自适应分割单元802连接用于接收对生理信号进行自适应分割的搜索分割方式。
自适应分割单元802根据搜索分割方式接收单元805接收到的搜索分割方式对获取到的生理信号进行搜索分割，其具体实现同上述方法的处理，不再赘述。
本领域普通技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，所述的程序可以在存储于一计算机可读取存储介质中，所述的存储介质，如ROM/RAM、磁盘、光盘等，该程序用来执行如下步骤：
获取生理信号；
对获取到的生理信号进行自适应分割，以得到相应的均匀段；
对得到的均匀段进行压缩。
在本发明实施例中，通过利用生理信号的局部物理特性的一致性与不同部分之间的差异，从语义压缩的角度进行数据压缩，具体就是利用生理信号局部特性的一致性，对生理信号的不同均匀段进行自适应的分割段，对不同的分割均匀段采用最小的有效位宽进行存储，从而达到较大程度的数据压缩，并完整的保真了生理信号。另外，由于该方法考虑到信号的局部一致性，对不同的分割均匀段采用不同的数据组织方式，相比简单的选择一种压缩方法能达到更高的压缩性能。
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。

Claims (20)

  1. 一种生理信号压缩方法，其特征在于，所述方法包括：
    获取生理信号；
    对获取到的生理信号进行自适应分割，以得到相应的均匀段；
    对得到的均匀段进行压缩。
  2. 如权利要求1所述的方法，其特征在于，采用自适应基线搜索分割方式、自适应一阶差分搜索分割方式，以及自适应多阶差分搜索分割方式中的一种或多种方式对所述获取到的生理信号进行自适应分割。
  3. 如权利要求2所述的方法，其特征在于，所述自适应多阶差分搜索分割方式为自适应n阶差分搜索分割方式或自适应m阶差分搜索分割方式，所述n=所述生理信号采样频率(hz)/50hz，所述m=所述生理信号采样频率(hz)/ 60hz。
  4. 如权利要求2所述的方法，其特征在于，当采用两种或以上的方式对所述生理信号进行自适应搜索分割后，从各搜索分割方式得到的均匀段中选择最优的均匀段作为所述对获取到的生理信号进行自适应分割，以得到相应的均匀段步骤最终得到的相应的均匀段。
  5. 如权利要求4所述的方法，其特征在于，根据各搜索分割方式得到的均匀段所需的存储位数选择最优的均匀段。
  6. 如权利要求2所述的方法，其特征在于，采用自适应基线搜索分割方式对获取到的生理信号进行自适应分割的步骤具体包括：
    对获取到的生理信号进行预设阈值的基线搜索，在阈值范围内搜索相应的均匀段；
    判断当前均匀段长度是否大于或等于预设长度阈值，如果是则分析当前均匀段分割是否优，否则，返回所述对获取到的生理信号进行预设阈值的基线搜索，在阈值范围内搜索相应的均匀段的步骤，重新继续搜索相应的均匀段；
    当前均匀段分割为优，则返回所述对获取到的生理信号进行预设阈值的基线搜索，在阈值范围内搜索相应的均匀段的步骤，继续搜索；
    当前均匀段分割不优，则保存当前分割信息，结束搜索。
  7. 如权利要求2所述的方法，其特征在于，采用自适应一阶或多阶差分搜索分割方式对获取到的生理信号进行自适应分割的步骤具体包括：
    对获取到的生理信号做一阶或多阶差分运算；
    在预设阈值范围内对差分信号进行搜索，得到差分信号对应的相应的均匀段；
    判断当前均匀段长度是否大于或等于预设长度阈值，如果是则分析当前均匀段分割是否优，否则，返回所述在预设阈值范围内对差分信号进行搜索，得到差分信号对应的相应的均匀段的步骤，继续搜索相应的均匀段；
    当当前均匀段分割为优，则返回所述在预设阈值范围内对差分信号进行搜索，得到差分信号对应的相应的均匀段的步骤，继续搜索；
    当当前均匀段分割不优，则保存当前分割信息，结束搜索。
  8. 如权利要求6或7所述的方法，其特征在于，当新增加的位数相比新增段头增加的位数更小，则判断当前均匀段分割为优。
  9. 如权利要求1至8任一项所述的方法，其特征在于，所述对得到的均匀段进行压缩步骤为：
    将得到的均匀段存储为段头与数据两部分。
  10. 如权利要求1至8任一项所述的方法，其特征在于，在所述获取生理信号的步骤获取的生理信号来源有多路时，在该步骤之后，所述对获取到的生理信号进行自适应分割，以得到相应的均匀段的步骤之前，所述方法还包括：
    对获取到的生理信号进行重排。
  11. 如权利要求2至8任一项所述的方法，其特征在于，在所述对获取到的生理信号进行自适应分割，以得到相应的均匀段的步骤之前，所述方法还包括：
    接收对生理信号进行自适应分割的搜索分割方式。
  12. 一种生理信号压缩系统，其特征在于，所述系统包括：
    生理信号获取单元，获取生理信号；
    自适应分割单元，与所述生理信号获取单元连接，对所述生理信号获取单元获取到的生理信号进行自适应分割，以得到相应的均匀段；
    压缩单元，与所述自适应分割单元连接，对所述自适应分割单元得到的均匀段进行压缩。
  13. 如权利要求12所述的系统，其特征在于，所述自适应分割单元采用自适应基线搜索分割方式、自适应一阶差分搜索分割方式，以及自适应多阶差分搜索分割方式中的一种或多种方式对所述获取到的生理信号进行自适应分割。
  14. 如权利要求13所述的系统，其特征在于，所述自适应多阶差分搜索分割方式为自适应n阶差分搜索分割方式或自适应m阶差分搜索分割方式，所述n=所述生理信号采样频率(hz)/50hz，所述m=所述生理信号采样频率(hz)/ 60hz。
  15. 如权利要求13所述的系统，其特征在于，所述自适应分割单元当采用两种或以上的方式对所述生理信号进行自适应搜索分割后，从各搜索分割方式得到的均匀段中选择最优的均匀段作为最终得到的相应的均匀段。
  16. 如权利要求15所述的系统，其特征在于，所述自适应分割单元根据各搜索分割方式得到的均匀段所需的存储位数选择最优的均匀段。
  17. 如权利要求12所述的系统，其特征在于，所述压缩单元将得到的均匀段存储为段头与数据两部分。
  18. 如权利要求12所述的系统，其特征在于，在所述生理信号获取单元获取的生理信号来源有多路时，所述系统还包括：
    数据重排单元，分别与所述生理信号获取单元、自适应分割单元连接，对所述生理信号获取单元获取到的生理信号进行重排。
  19. 如权利要求12所述的系统，其特征在于，所述系统还包括：
    搜索分割方式接收单元，与所述自适应分割单元连接，接收对生理信号进行自适应分割的搜索分割方式。
  20. 一种包含如权利要求11至19中任一项权利要求所述生理信号压缩系统的医疗设备。
PCT/CN2014/077334 2013-12-31 2014-05-13 一种生理信号压缩方法、系统及医疗设备 Ceased WO2015100910A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201310754568.8A CN104739509B (zh) 2013-12-31 2013-12-31 一种生理信号压缩方法、系统及医疗设备
CN201310754568.8 2013-12-31

Publications (1)

Publication Number Publication Date
WO2015100910A1 true WO2015100910A1 (zh) 2015-07-09

Family

ID=53493089

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2014/077334 Ceased WO2015100910A1 (zh) 2013-12-31 2014-05-13 一种生理信号压缩方法、系统及医疗设备

Country Status (2)

Country Link
CN (1) CN104739509B (zh)
WO (1) WO2015100910A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220061771A1 (en) * 2020-08-27 2022-03-03 Ricoh Company, Ltd. Method and apparatus for processing physiological signals and non-transitory computer-readable medium

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112863653B (zh) * 2021-03-01 2025-06-13 武汉中旗生物医疗电子有限公司 一种心电数据压缩方法及装置
CN121418212B (zh) * 2025-12-29 2026-04-21 中国科学院苏州生物医学工程技术研究所 边缘侧多模态信号分级加密与压缩传输方法及系统、介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5838392A (en) * 1998-03-19 1998-11-17 United Microelectronics Corp. Adaptive block-matching motion estimator with a compression array for use in a video coding system
WO2007011080A1 (en) * 2005-07-16 2007-01-25 Lg Electronics Inc. Apparatus and method of encoding and decoding audio signal
CN101099669A (zh) * 2007-07-13 2008-01-09 天津大学 基于最优时频空间结构码的心电数据压缩方法和解码方法
CN101346982A (zh) * 2005-12-21 2009-01-14 松下电器产业株式会社 图像压缩装置、图像拉伸装置以及超声波诊断装置
CN102332162A (zh) * 2011-09-19 2012-01-25 西安百利信息科技有限公司 基于人工神经网络的医学图像兴趣区自动识别和分级压缩方法
CN102610232A (zh) * 2012-01-10 2012-07-25 天津大学 一种自适应音频感知响度调整方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050119866A1 (en) * 2003-11-14 2005-06-02 Zaleski John R. Medical parameter processing system
WO2011145377A1 (ja) * 2010-05-17 2011-11-24 コニカミノルタエムジー株式会社 放射線画像処理装置
CN102347944B (zh) * 2011-07-22 2014-09-03 哈尔滨工业大学深圳研究生院 基于小波算法的心电信号传输方法和系统

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5838392A (en) * 1998-03-19 1998-11-17 United Microelectronics Corp. Adaptive block-matching motion estimator with a compression array for use in a video coding system
WO2007011080A1 (en) * 2005-07-16 2007-01-25 Lg Electronics Inc. Apparatus and method of encoding and decoding audio signal
CN101346982A (zh) * 2005-12-21 2009-01-14 松下电器产业株式会社 图像压缩装置、图像拉伸装置以及超声波诊断装置
CN101099669A (zh) * 2007-07-13 2008-01-09 天津大学 基于最优时频空间结构码的心电数据压缩方法和解码方法
CN102332162A (zh) * 2011-09-19 2012-01-25 西安百利信息科技有限公司 基于人工神经网络的医学图像兴趣区自动识别和分级压缩方法
CN102610232A (zh) * 2012-01-10 2012-07-25 天津大学 一种自适应音频感知响度调整方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
WANG, LU ET AL.: "Adaptive Fractal Compression Technology of Medical Image", CHINA PRACTICAL MEDICINE, vol. 5, no. 1, 31 January 2010 (2010-01-31), pages 75 - 76 *
YANG, XUEMEI ET AL.: "Study on auto-adaptive blocking image compression arithmetic", AUTOMATION AND INSTRUMENTATION, no. 5, 31 December 2011 (2011-12-31), pages 7 - 9 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220061771A1 (en) * 2020-08-27 2022-03-03 Ricoh Company, Ltd. Method and apparatus for processing physiological signals and non-transitory computer-readable medium
US12115004B2 (en) * 2020-08-27 2024-10-15 Ricoh Company, Ltd. Method and apparatus for processing physiological signals and non-transitory computer-readable medium

Also Published As

Publication number Publication date
CN104739509A (zh) 2015-07-01
CN104739509B (zh) 2017-11-07

Similar Documents

Publication Publication Date Title
US8138954B2 (en) Method and system for efficient data transmission with server side de-duplication
RU2377670C2 (ru) Сжатие данных
US8578058B2 (en) Real-time multi-block lossless recompression
JP6021498B2 (ja) データ圧縮装置、データ圧縮プログラム、データ圧縮システム、データ圧縮方法、データ伸張装置、データ圧縮伸張システム、および圧縮データのデータ構造
WO2015100910A1 (zh) 一种生理信号压缩方法、系统及医疗设备
KR101410249B1 (ko) Ｃｒａｎ에서 ｄｕ와 ｒｕ간 ｉ/ｑ 데이터 압축 및 그 해제 방법
CN103457903A (zh) 一种数字式水下语音通信装置及水下语音通信方法
WO2016072628A1 (ko) 오디오 신호를 복원하는 방법 및 장치
US10601441B2 (en) Efficient software closing of hardware-generated encoding context
JP2018534811A (ja) ロスレスデータ圧縮
JP4105138B2 (ja) プロトコルマーカーに基づく情報の識別のための技術
EP3507799A1 (en) Quantizer with index coding and bit scheduling
CN118762703B (zh) 可接入Dante音频网络的蓝牙音频墙插型编解码器
JP7008870B2 (ja) ビデオを録画する方法および装置
Alsenwi et al. Hybrid compression technique with data segmentation for electroencephalography data
JPH07212241A (ja) 実時間デジタル音声圧縮解凍システム
CN103152606B (zh) 视频文件处理方法及装置、系统
Blair et al. Slipstream: High-performance lossless compression for streaming synchronized waveform monitoring data
WO2020211000A1 (zh) 数据解压缩的装置与方法
US20070036228A1 (en) Method and apparatus for audio encoding and decoding
JP6125807B2 (ja) データ圧縮装置、データ圧縮プログラム、データ圧縮システム、データ圧縮方法、データ伸張装置、およびデータ圧縮伸張システム
JP2007537642A (ja) Ｘｍｌデータの構造化されたブロック単位の圧縮及び解凍方法及び装置
CN100401671C (zh) 一种文件传送方法
JP2016031509A (ja) データ圧縮装置及びデータ圧縮方法
CN114038526B (zh) 一种推送平台数据的转换器

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 14876291

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205 DATED 10.11.2016)

122 Ep: pct application non-entry in european phase

Ref document number: 14876291

Country of ref document: EP

Kind code of ref document: A1